   _      _  _  _    _      ___  
  /_\    | || || |  /_\    | __| 
 / _ \   | \/ \/ | / _ \   | _|  
/_/ \_\   \_/\_/  /_/ \_\  |_        Agent Well-Architected Framework

AWAF Assessment: awaf-cli
AWAF v1.0 | 2026-03-29 | anthropic / claude-haiku-4-5-20251001
========================================

Overall Score: 4/100 -- Not Ready
Critical gaps across multiple pillars. Major rework required.

Scale: Production Ready 85-100 | Near Ready 70-84 | Needs Work 50-69
       High Risk 25-49 | Not Ready 0-24
Foundation <40 = automatic FAIL. Tier 2 pillars carry 1.5x weight.

----------------------------------------
+======================+==========+==============+==============+=========+
| Pillar               | Score    | Progress     | Confidence   |  Status |
+======================+==========+==============+==============+=========+
| TIER 0 -- FOUNDATION                                                    |
+----------------------+----------+--------------+--------------+---------+
| Foundation           | 6/100    | [#         ] | verified     |    FAIL |
+======================+==========+==============+==============+=========+
| TIER 1 -- CLOUD WAF ADAPTED                                             |
+----------------------+----------+--------------+--------------+---------+
| Op. Excellence       | 0/100    | [          ] | verified     |         |
| Security             | 14/100   | [#         ] | verified     |         |
| Reliability          | 0/100    | [          ] | verified     |         |
| Performance          | 0/100    | [          ] | verified     |         |
| Cost Optim.          | 23/100   | [##        ] | verified     |         |
| Sustainability       | 0/100    | [          ] | verified     |         |
+======================+==========+==============+==============+=========+
| TIER 2 -- AGENT-NATIVE (1.5x weight)                                    |
+----------------------+----------+--------------+--------------+---------+
| Reasoning Integ.     | 0/100    | [          ] | verified     |    1.5x |
| Controllability      | 0/100    | [          ] | verified     |    1.5x |
| Context Integrity    | 5/100    | [          ] | verified     |    1.5x |
+======================+==========+==============+==============+=========+

----------------------------------------
FILES ANALYZED: 3 files

----------------------------------------
FINDINGS (ordered by severity)
  [Critical]  Foundation          Hard structural dependency on session-
                                  service at startup. agent.py line 30-33:
                                  load_session_context() calls sys.exit(1) on
                                  any failure (network, timeout, 404, auth).
                                  Agent cannot boot without external service.
                                  No fallback context, no degraded mode, no
                                  default preferences. This is a Foundation
                                  Fail -- the agent does not own its domain
                                  end-to-end.
  [Critical]  Foundation          Agent owns no persistent state. agent.py
                                  line 13: conversation history is a module-
                                  level list in process memory. Restart loses
                                  all context. Session preferences are fetched
                                  at runtime from session-service (line 18-24)
                                  and not cached. Agent cannot function as an
                                  autonomous slice.
  [Critical]  Foundation          Blast radius uncontained. Failure of
                                  session-service at http://session-
                                  service:8080 (agent.py line 20) causes
                                  immediate agent failure. No circuit breaker,
                                  no timeout, no fallback. A single upstream
                                  service outage takes down the agent
                                  completely.
  [Critical]  Op. Excellence      No SLOs defined. Agent has no latency
                                  targets, success rate thresholds, or cost
                                  budgets. Production impact: no basis for
                                  alerting, no way to detect degradation.
                                  Evidence: AWAF_SCORE.md notes 'No SLOs'
                                  under Op. Excellence; agent.py has no
                                  instrumentation.
  [Critical]  Op. Excellence      No runbooks for failure modes. Agent fails
                                  hard on session-service unavailability
                                  (sys.exit(1)), Anthropic API timeouts, and
                                  unbounded context growth. No documented
                                  recovery procedures. Evidence: agent.py
                                  lines 24-26 (sys.exit on load failure); no
                                  runbook artifacts provided.
  [Critical]  Op. Excellence      No structured logging. Agent uses print() to
                                  stderr only (agent.py lines 48, 52). No
                                  session_id, request_id, timestamp, or log
                                  level. Debugging a production failure
                                  requires manual reproduction. Evidence:
                                  agent.py lines 48, 52; README.md silent on
                                  observability.
  [Critical]  Op. Excellence      No alerting configured. No PagerDuty,
                                  CloudWatch, Datadog, or OpsGenie
                                  integration. Error rates, latency spikes,
                                  and budget overruns go undetected. Evidence:
                                  no alerting config files; AWAF_SCORE.md
                                  notes 'no alerting'.
  [Critical]  Security            User questions flow directly into LLM prompt
                                  without sanitization. Malicious input
                                  (prompt injection, jailbreak attempts) is
                                  not validated or escaped before being
                                  appended to history and sent to Claude
                                  (agent.py line 35: history.append({'role':
                                  'user', 'content': question})). An attacker
                                  can craft questions to override system
                                  instructions or extract sensitive context.
  [Critical]  Security            Session service called over unencrypted HTTP
                                  with authentication token in header.
                                  INTERNAL_TOKEN is transmitted in plaintext
                                  and visible to any network observer
                                  (agent.py line 25: http://session-
                                  service:8080). Token compromise enables
                                  unauthorized session access.
  [Critical]  Security            No kill switch or emergency stop mechanism
                                  in code. Agent runs indefinitely until
                                  Ctrl+C; no SIGTERM handler, no programmatic
                                  flag to halt execution, no timeout on
                                  blocking calls. A runaway loop or hung
                                  dependency cannot be terminated remotely
                                  (agent.py lines 58-60).
  [Critical]  Reliability         Hard structural dependency on session-
                                  service with no fallback. agent.py line 34
                                  calls sys.exit(1) if session service is
                                  unreachable at startup. No degraded mode, no
                                  default context, no circuit breaker. Single
                                  upstream failure terminates the entire
                                  agent.
  [Critical]  Reliability         No timeout on external HTTP calls. agent.py
                                  line 13 requests.get() has no timeout
                                  parameter; will hang indefinitely if
                                  session-service is slow or unresponsive.
                                  Same risk on Anthropic SDK call (line 27) --
                                  no timeout on CLIENT.messages.create().
  [Critical]  Reliability         Unbounded context growth with no overflow
                                  detection. agent.py line 20 appends to
                                  history list without cap; no check before
                                  sending to API. Long sessions will exceed
                                  context window, causing silent truncation or
                                  API errors.
  [Critical]  Performance         Model selection is static and unconditional.
                                  agent.py line 13 sets MODEL = 'claude-
                                  opus-4-6' globally; ask() function (line 20)
                                  always uses this model regardless of
                                  question complexity. Simple factual queries
                                  (e.g., 'What is 2+2?') consume the same
                                  token budget and latency as complex
                                  reasoning tasks. No routing logic, no cost-
                                  aware fallback to claude-haiku or claude-
                                  sonnet.
  [Critical]  Performance         Context window overflow guaranteed. agent.py
                                  line 24 appends every user question and
                                  assistant response to the unbounded history
                                  list. No pruning, no sliding window, no
                                  summarization. Long sessions will exceed
                                  claude-opus-4-6's context limit (200k
                                  tokens) and either fail or silently truncate
                                  early turns, degrading reasoning quality.
  [Critical]  Cost Optim.         No session budget enforced. Agent has no
                                  `SESSION_TOKEN_LIMIT` or spend cap; while
                                  loop in main() (agent.py:60-67) will
                                  continue indefinitely, consuming tokens at
                                  ~$0.015 per 1K input tokens (Opus 4.6). A
                                  user can exhaust $100+ in a single session
                                  without warning.
  [Critical]  Cost Optim.         No loop detection. Main loop
                                  (agent.py:60-67) has no iteration counter,
                                  timeout, or max-turn guard. Pathological
                                  input (e.g., user repeatedly asking
                                  identical questions) will accumulate
                                  unbounded history and trigger context window
                                  overflow, forcing expensive re-prompting or
                                  failure.
  [Critical]  Sustainability      Agent always uses claude-opus-4-6 (most
                                  capable, highest cost model) for all queries
                                  regardless of complexity. A simple factual
                                  lookup or clarification question incurs the
                                  same token cost as a complex reasoning task.
                                  agent.py line 13 hardcodes MODEL = 'claude-
                                  opus-4-6' with no conditional logic.
  [Critical]  Reasoning Integ.    No eval framework present. Agent has no
                                  automated tests for reasoning accuracy, tool
                                  selection, or hallucination detection. ask()
                                  in agent.py returns LLM output directly
                                  without validation or uncertainty
                                  quantification.
  [Critical]  Reasoning Integ.    No reasoning trace or chain-of-thought
                                  logging. LLM responses are not inspected for
                                  reasoning quality; no audit trail of how the
                                  agent arrived at answers. history list in
                                  agent.py is in-process only and discarded on
                                  restart.
  [Critical]  Controllability     No programmatic kill switch. Agent only
                                  terminates on Ctrl+C (OS signal) or
                                  exception. No code path exists to gracefully
                                  stop the agent mid-conversation or cancel an
                                  in-flight API call. (agent.py: no signal
                                  handler, no kill flag, no timeout on
                                  CLIENT.messages.create() call at line 26)
  [Critical]  Controllability     No pause/resume capability. Conversation
                                  history is volatile in-process list
                                  (agent.py line 11); no checkpoint mechanism
                                  exists. Restart loses all context. Cannot
                                  pause mid-question and resume later.
                                  (agent.py: history list has no persistence,
                                  no state serialization)
  [Critical]  Context Integrity   Unbounded context accumulation: history list
                                  grows without limit across the session. No
                                  pruning, summarization, or window-aware
                                  truncation. Will cause context window
                                  overflow and silent degradation in long
                                  conversations. (agent.py line 11, ask()
                                  function)
  [Critical]  Context Integrity   No input sanitization: user questions and
                                  session context (language, expertise) flow
                                  directly into the system prompt without
                                  validation or escaping. Enables prompt
                                  injection attacks via malicious session data
                                  or user input. (agent.py lines 24-26, 33)
  [Critical]  Context Integrity   Stale context never refreshed: session
                                  context loaded once at startup and never re-
                                  fetched. User preferences or conversation
                                  state changes on the backend are invisible
                                  to the agent. (agent.py line 40,
                                  load_session_context called only in main())
  [High    ]  Foundation          Slice boundary not documented or enforced.
                                  README.md and agent.py contain no
                                  architecture diagram, no explicit ownership
                                  boundary, no data contract definition. The
                                  relationship between agent and session-
                                  service is implicit and tightly coupled.
  [High    ]  Foundation          Inter-agent communication is structural, not
                                  deliberate. agent.py line 18-24: session-
                                  service call is required on every startup
                                  and embedded in the main control flow. No
                                  explicit contract, no versioning, no async
                                  fallback. This is coupling, not integration.
  [High    ]  Op. Excellence      No evals defined or scheduled. Agent returns
                                  all Claude responses as-is with no
                                  hallucination measurement, model selection
                                  validation, or behavior regression
                                  detection. Evidence: no evals/ directory;
                                  AWAF_SCORE.md notes 'No evals'.
  [High    ]  Op. Excellence      No postmortem process. No template, no
                                  incident records, no blameless review
                                  mechanism. Operational learning is blocked.
                                  Evidence: no postmortem artifacts provided;
                                  README.md and agent.py silent on incident
                                  response.
  [High    ]  Security            Blast radius of compromised agent is
                                  unbounded. No rate limits, token budgets, or
                                  API scope restrictions. A compromised agent
                                  can exhaust Anthropic quota, enumerate all
                                  sessions via the session service, or
                                  exfiltrate user context without triggering
                                  alerts (agent.py lines 37-45, 24-31).
  [High    ]  Security            Single INTERNAL_TOKEN used for all session
                                  service operations with no per-session or
                                  per-operation scoping. Token grants access
                                  to any session endpoint; compromise of the
                                  token grants full access to the session
                                  service (agent.py line 26).
  [High    ]  Security            Credentials read directly from environment
                                  variables with no rotation, audit trail, or
                                  centralized secrets management. No
                                  integration with AWS Secrets Manager,
                                  HashiCorp Vault, or Azure Key Vault.
                                  Credential compromise is undetectable and
                                  unrecoverable (agent.py lines 1-2, README).
  [High    ]  Reliability         No retry logic or circuit breaker. Both
                                  requests.get() (line 13) and
                                  CLIENT.messages.create() (line 27) will fail
                                  immediately on transient errors. No
                                  exponential backoff, no retry budget.
  [High    ]  Reliability         No checkpoint/resume mechanism. Conversation
                                  history is in-process memory only (agent.py
                                  line 9). Agent restart loses all context. No
                                  persistence layer, no session recovery.
  [High    ]  Reliability         Silent partial failures possible. ask()
                                  function (line 19) appends user question to
                                  history before API call succeeds. If
                                  Anthropic API fails mid-response, history is
                                  corrupted with incomplete assistant message.
  [High    ]  Performance         No latency instrumentation or SLO. agent.py
                                  contains no timing code around
                                  CLIENT.messages.create() (line 28) or
                                  requests.get() (line 16). No latency
                                  dashboard, no p50/p95 targets, no alerting
                                  on slow responses. Operators cannot detect
                                  performance regressions.
  [High    ]  Performance         No caching of responses. Identical questions
                                  asked in the same session will trigger a
                                  full LLM call each time (line 28). No
                                  semantic caching, no Redis, no in-memory
                                  deduplication. Wastes tokens and increases
                                  latency for repeated queries.
  [High    ]  Cost Optim.         Token usage not tracked. Response object
                                  from `CLIENT.messages.create()`
                                  (agent.py:48) includes `usage` metadata
                                  (input_tokens, output_tokens), but code
                                  discards it. No per-session cost
                                  calculation, no cost trend visibility, no
                                  basis for alerting.
  [High    ]  Cost Optim.         No cost alerts or budget service
                                  integration. No connection to AWS Budgets,
                                  Azure Cost Management, or internal cost
                                  tracking. Overspend is discovered only via
                                  billing invoice, weeks after the fact.
  [High    ]  Sustainability      No result caching mechanism. Identical
                                  questions asked in the same session will
                                  trigger identical API calls and incur
                                  identical costs. No memoization, no cache
                                  key generation, no TTL-based invalidation.
  [High    ]  Sustainability      No batching of API calls. Session context is
                                  fetched once at startup (agent.py line 47),
                                  but each user question triggers a separate
                                  synchronous Anthropic API call (agent.py
                                  line 35). No opportunity to batch multiple
                                  questions or defer non-critical calls.
  [High    ]  Reasoning Integ.    Hallucination rate unmeasured. No red team
                                  results, no adversarial testing, no metrics
                                  dashboard. Agent will confidently return
                                  incorrect information with no detection
                                  mechanism.
  [High    ]  Reasoning Integ.    No uncertainty surfacing. ask() returns
                                  response.content[0].text as a flat string.
                                  If the LLM is uncertain or contradicts
                                  itself, the agent has no mechanism to detect
                                  or communicate this to the user.
  [High    ]  Reasoning Integ.    Provenance tracking absent. Session context
                                  from load_session_context() is not logged or
                                  validated before injection into the system
                                  prompt. No audit trail of which context
                                  fields influenced which responses.
  [High    ]  Controllability     No human-in-the-loop checkpoints. All user
                                  questions flow directly into LLM prompt
                                  without review, validation, or approval
                                  gate. No mechanism to inspect or reject a
                                  question before it reaches Claude. (agent.py
                                  ask() function lines 20-31: question
                                  appended to history and sent immediately)
  [High    ]  Controllability     No approval gate before irreversible
                                  actions. Responses are printed directly to
                                  stdout without review, confirmation, or
                                  audit. User cannot reject or modify an
                                  answer before it is displayed. (agent.py
                                  line 35: print(f'\nAgent: {answer}\n') with
                                  no confirmation step)
  [High    ]  Controllability     No auditable action log. No structured
                                  logging of questions, answers, or API calls.
                                  History list is in-process memory only and
                                  lost on restart. No audit trail for
                                  compliance or incident investigation.
                                  (agent.py: no logging module, no audit
                                  table, no CloudTrail equivalent)
  [High    ]  Context Integrity   No context window tracking: no token
                                  counting, no monitoring of message size, no
                                  warning before overflow. Agent will silently
                                  drop context or fail when approaching
                                  limits. (agent.py, no token_counter or
                                  window_monitor)
  [High    ]  Context Integrity   No epistemic markers: all context treated as
                                  equally reliable. Agent does not distinguish
                                  between user-provided facts, session
                                  metadata, and inferred state. Responses
                                  carry no uncertainty signals. (agent.py
                                  lines 24-26, system prompt)
  [High    ]  Context Integrity   Conversation state not persisted: history is
                                  in-process memory only. A restart or crash
                                  loses all conversation context. No
                                  scratchpad, memory store, or session replay
                                  mechanism. (agent.py line 11, history:
                                  list[dict] = [])
  [Medium  ]  Performance         No parallelization or batching. ask()
                                  function (line 20) is synchronous and
                                  sequential: load session context, call LLM,
                                  append to history. No async/await, no
                                  concurrent requests, no message batching.
                                  Each question blocks until the LLM response
                                  arrives.
  [Medium  ]  Cost Optim.         Model selection is static. Agent always uses
                                  `claude-opus-4-6` (agent.py:7), the most
                                  expensive model (~$15/1M input tokens).
                                  Simple factual questions could use `claude-
                                  haiku` (~$0.80/1M input tokens) and save 95%
                                  on cost for that query.
  [Medium  ]  Sustainability      No input deduplication or change detection.
                                  Every question, even if identical to a prior
                                  one, is appended to history and sent to the
                                  API. No hash-based skip logic or input
                                  comparison.
  [Medium  ]  Controllability     No runtime scope restriction. Model
                                  (`claude-opus-4-6`) and max_tokens (2048)
                                  are hardcoded. Cannot adjust agent behavior
                                  without code change and redeployment.
                                  (agent.py lines 6, 25: MODEL and max_tokens
                                  are constants)

----------------------------------------
RECOMMENDATIONS
  Foundation            Refactor load_session_context() to return sensible
                        defaults on failure instead of sys.exit(1). agent.py
                        line 30-33: catch requests.RequestException and return
                        {'language': 'en', 'expertise': 'general'} or load
                        from a local cache file. This unblocks the agent to
                        function independently.
  Foundation            Move conversation history to a local file or in-memory
                        cache with optional persistence. agent.py line 13:
                        replace module-level list with a SessionMemory class
                        that can serialize to disk on shutdown and restore on
                        startup. Agent owns its context.
  Foundation            Add explicit timeout and circuit breaker to session-
                        service calls. agent.py line 20: add timeout=5 to
                        requests.get(); wrap in a retry decorator with max 2
                        attempts, then fall back to defaults. Contain the
                        blast radius.
  Foundation            Document the slice boundary and data contract. Create
                        ARCHITECTURE.md at repo root: define what the agent
                        owns (conversation loop, question routing, response
                        formatting), what it delegates (user preferences,
                        session state), and the explicit contract with
                        session-service (request/response schema, error
                        handling, SLA).
  Op. Excellence        Create docs/slos.md defining: latency p99 < 5s,
                        success rate > 99%, cost < $0.10/session. Add
                        CloudWatch alarms for each. Owner: platform team.
  Op. Excellence        Create docs/runbooks/ with procedures for: (1)
                        session-service down -> use cached context or default
                        prefs, (2) Anthropic timeout -> retry with exponential
                        backoff, (3) context overflow -> truncate history.
                        Owner: on-call engineer.
  Op. Excellence        Replace print() with structured logging. Add import
                        logging; configure JSON formatter with session_id,
                        request_id, timestamp. Example: agent.py line 48 ->
                        logger.info('answer_generated',
                        session_id=args.session,
                        tokens=response.usage.output_tokens). Owner: agent
                        maintainer.
  Op. Excellence        Add CloudWatch/Datadog monitors: ErrorRate > 5%,
                        Latency p99 > 5s, TokensPerSession > 50k. Route to
                        PagerDuty on-call. Owner: platform team.
  Op. Excellence        Create evals/test_model_selection.py with unit tests
                        for model choice logic (e.g., haiku for short Q,
                        sonnet for long Q). Run in CI on every commit. Owner:
                        agent maintainer.
  Op. Excellence        Create docs/postmortem_template.md with sections:
                        timeline, root cause, impact, action items. Link from
                        README.md. Owner: platform team.
  Security              Implement input sanitization in ask() function
                        (agent.py line 35). Validate question length, reject
                        control characters, and escape special characters
                        before appending to history. Consider using a prompt
                        injection filter library (e.g., llm-guard).
  Security              Switch session service endpoint from http:// to
                        https:// and enforce certificate validation (agent.py
                        line 25). Migrate INTERNAL_TOKEN to a short-lived
                        bearer token issued by an identity provider (e.g.,
                        OAuth2, mTLS).
  Security              Add SIGTERM handler and kill flag to main loop
                        (agent.py line 58). Implement
                        signal.signal(signal.SIGTERM, lambda s, f:
                        setattr(sys, '_exit_flag', True)) and check flag in
                        while loop.
  Security              Implement SESSION_TOKEN_LIMIT guard and per-session
                        rate limiting (agent.py line 37). Add token counter
                        incremented after each API call; break loop and alert
                        if limit exceeded. Add exponential backoff on session
                        service 429 responses.
  Security              Scope INTERNAL_TOKEN to specific session ID and read-
                        only operations. Migrate to per-session tokens issued
                        by session service at startup (agent.py line 24).
                        Implement least-privilege: agent should only read its
                        own session, not enumerate or modify others.
  Security              Integrate with AWS Secrets Manager or HashiCorp Vault
                        for credential rotation and audit (agent.py lines
                        1-2). Load ANTHROPIC_API_KEY and INTERNAL_TOKEN from
                        secrets manager at startup; implement automatic
                        rotation every 30 days.
  Reliability           Add timeout to requests.get() call: agent.py line 13,
                        change to `requests.get(..., timeout=5)`. Add timeout
                        to Anthropic client: agent.py line 27, add
                        `timeout=30` to CLIENT.messages.create().
  Reliability           Implement fallback in load_session_context(): agent.py
                        line 11, return default context dict (language='en',
                        expertise='general') on exception instead of
                        sys.exit(1). Allow agent to run with degraded
                        preferences.
  Reliability           Cap history before each API call: agent.py line 19,
                        add logic to trim history to last N turns (e.g., 10)
                        before appending new message. Prevents context window
                        overflow.
  Reliability           Add retry logic with exponential backoff: wrap
                        requests.get() and CLIENT.messages.create() calls in a
                        retry decorator (e.g., tenacity library) with
                        max_retries=3, backoff_factor=2.
  Reliability           Persist conversation history: add a checkpoint
                        mechanism (e.g., write history to local SQLite or
                        session service) after each successful ask() call.
                        Support resume on restart.
  Reliability           Fail loudly on partial state: agent.py line 20, only
                        append assistant response to history AFTER successful
                        API response. Validate response.content[0].text is
                        non-empty before appending.
  Performance           Implement model routing in ask() function (agent.py
                        line 20): measure question length/complexity (token
                        count or keyword heuristics); route to claude-haiku
                        for <500 tokens, claude-sonnet for 500-2000, claude-
                        opus-4-6 only for >2000 or explicit reasoning tasks.
                        Add a --model-override flag for testing.
  Performance           Add context pruning before each API call (agent.py
                        line 24-28): cap history to last N turns (e.g., 10
                        turns = 20 messages) or summarize turns older than M
                        minutes. Implement a sliding window that removes
                        oldest user/assistant pairs when len(history) >
                        threshold.
  Performance           Instrument latency with timing decorators or context
                        managers (agent.py line 20, 28): wrap
                        CLIENT.messages.create() and requests.get() with
                        time.perf_counter(); log elapsed_ms to structured logs
                        with session_id. Define SLO: p50 < 2s, p95 < 5s for
                        typical questions.
  Performance           Add a simple in-memory cache (agent.py line 10):
                        hash(question) -> answer dict; check cache before
                        calling ask(). Expire entries after 1 hour or on
                        session reload. For production, upgrade to Redis with
                        semantic similarity matching.
  Performance           Convert ask() to async (agent.py line 20): use asyncio
                        and httpx to parallelize session service fetch and LLM
                        call if they are independent. Measure latency
                        improvement; document in a performance benchmark file.
  Cost Optim.           Add `SESSION_TOKEN_LIMIT` env var (default 10000
                        tokens) and track cumulative input+output tokens.
                        Break the main loop when exceeded, with a user-facing
                        message. Implement in agent.py:ask() by capturing
                        `response.usage.input_tokens +
                        response.usage.output_tokens` and maintaining a
                        session total.
  Cost Optim.           Add max-turn guard to main loop (agent.py:60-67):
                        `turn_count = 0; MAX_TURNS = 50` (or env var).
                        Increment on each question; exit with 'Session limit
                        reached' when exceeded.
  Cost Optim.           Log token usage and cost per turn. After `response =
                        CLIENT.messages.create()` (agent.py:48), extract
                        `response.usage` and log: `{session_id, turn,
                        input_tokens, output_tokens, cost_usd}`. Write to
                        structured log or cost tracking service (e.g.,
                        Datadog, CloudWatch).
  Cost Optim.           Implement model selection logic: if question length <
                        100 chars and no prior context, use `claude-haiku`;
                        else use `claude-sonnet`. Reserve `claude-opus-4-6`
                        for multi-turn reasoning. Add to agent.py:ask() before
                        `CLIENT.messages.create()` call.
  Cost Optim.           Integrate with AWS Budgets or equivalent: create a
                        budget alert for the service account running this
                        agent, set threshold at 80% of monthly spend limit,
                        and route alerts to on-call Slack channel.
  Sustainability        Implement model routing: add a function that
                        classifies question complexity (keyword-based or via a
                        lightweight heuristic) and routes to claude-haiku for
                        simple queries, claude-sonnet for medium, and claude-
                        opus-4-6 only for complex reasoning. Update agent.py
                        ask() function to call this router before
                        CLIENT.messages.create().
  Sustainability        Add a simple in-memory cache with input hash as key:
                        before calling ask(), compute hash(question) and check
                        if result exists in a dict. Store results with a TTL
                        (e.g., 1 hour) or session-scoped lifetime. Update
                        agent.py ask() function to check cache first.
  Sustainability        Implement input deduplication: before appending to
                        history, check if the question is identical to the
                        last N questions (e.g., last 5). If so, return the
                        cached answer instead of calling the API. Add a simple
                        equality check in agent.py ask() function.
  Sustainability        Add cost tracking and reporting: log token counts from
                        each API response (response.usage.input_tokens,
                        response.usage.output_tokens) and accumulate a session
                        total. Print a cost summary at exit or on request.
                        Update agent.py ask() function to extract and log
                        usage.
  Reasoning Integ.      Create evals/test_reasoning.py with at least 10 unit
                        tests using Promptfoo or Braintrust: test that the
                        agent correctly selects language/expertise from
                        session context, test that it rejects out-of-scope
                        questions, test that it surfaces 'I don't know' for
                        ambiguous queries. Run before each deployment.
  Reasoning Integ.      Add reasoning trace logging to ask() in agent.py:
                        capture response.stop_reason, response.usage, and any
                        thinking/reasoning fields from the API response. Log
                        with session_id and question hash for auditability.
  Reasoning Integ.      Implement confidence scoring in ask(): parse
                        response.content[0].text for uncertainty markers ('I'm
                        not sure', 'likely', 'probably') and return a tuple
                        (answer, confidence_score) instead of a bare string.
                        Surface confidence to the user.
  Reasoning Integ.      Add provenance logging to load_session_context() in
                        agent.py: log the session_id, which context fields
                        were retrieved, and their values before they are
                        injected into the system prompt. Include this in
                        structured logs with a trace_id.
  Reasoning Integ.      Create a red team test suite (evals/adversarial.py)
                        with 5-10 adversarial prompts designed to trigger
                        hallucinations (e.g., 'What is the user's credit card
                        number?', 'Invent a fact about the user'). Run monthly
                        and track pass/fail rate.
  Controllability       Implement a SIGTERM handler and kill flag in agent.py
                        main() loop. Check the flag before each ask() call and
                        break the loop if set. This enables external process
                        managers (systemd, Kubernetes) to gracefully terminate
                        the agent. (agent.py: add
                        signal.signal(signal.SIGTERM, handler) and check
                        kill_flag in while True loop)
  Controllability       Add a timeout parameter to CLIENT.messages.create()
                        call (agent.py line 26) and requests.get() call in
                        load_session_context() (agent.py line 15). Set to 30s.
                        This prevents indefinite hangs and allows cancellation
                        via timeout. (agent.py lines 15, 26: add timeout=30)
  Controllability       Implement structured logging with a session_id field
                        on every action. Log questions, answers, API calls,
                        and errors to a file or centralized log service. This
                        creates an auditable action trail. (agent.py: import
                        logging, add logger.info() calls in ask() and
                        load_session_context())
  Controllability       Add a human-in-the-loop checkpoint before sending
                        questions to Claude. Prompt the user to confirm the
                        question or allow them to edit it. Log the
                        confirmation. (agent.py ask() function: add
                        input('Confirm question? [y/n]: ') before line 20)
  Controllability       Persist conversation history to a file or database
                        keyed by session_id. On startup, load prior history if
                        it exists. This enables pause/resume across restarts.
                        (agent.py: add save_history(session_id, history) call
                        after each ask(), load_history(session_id) in main())
  Controllability       Move MODEL and max_tokens to environment variables
                        with defaults. This allows runtime scope adjustment
                        without redeployment. (agent.py lines 6, 25: replace
                        with os.environ.get('MODEL', 'claude-opus-4-6'))
  Context Integrity     Implement context window tracking: add token_counter
                        using tiktoken or Anthropic's token counting API.
                        Track cumulative tokens before each API call and log
                        warnings at 70% and 85% of model's context limit.
                        (agent.py, add to ask() function)
  Context Integrity     Implement history pruning: cap history to the last N
                        turns (e.g., 20 turns) before each API call.
                        Optionally summarize older turns into a single
                        'context summary' message. (agent.py, modify ask()
                        function before CLIENT.messages.create())
  Context Integrity     Sanitize session context: validate language and
                        expertise fields against a whitelist; escape any user-
                        controlled strings before interpolating into system
                        prompt. Use f-string escaping or a templating library.
                        (agent.py, modify load_session_context() return or
                        ask() system prompt construction)
  Context Integrity     Refresh session context periodically: re-fetch session
                        context every N turns or on explicit user command.
                        Compare new context to cached context and alert if
                        preferences changed. (agent.py, modify main() loop to
                        call load_session_context() every 5-10 turns)
  Context Integrity     Persist conversation state: store history to a local
                        SQLite DB or session service after each turn. On
                        restart, reload history from storage. Include a
                        session_id in all stored records. (agent.py, add
                        persist_history() and load_history() functions; call
                        after each ask())
  Context Integrity     Add epistemic markers to system prompt: instruct the
                        model to prefix uncertain statements with 'I'm not
                        certain, but...' and to flag when it is reasoning
                        beyond the provided context. (agent.py, modify system
                        prompt in ask() function)

----------------------------------------
TO IMPROVE THIS ASSESSMENT
  Provide ARCHITECTURE.md with a diagram showing agent slice, owned tools, and
  external dependencies with explicit contracts.
  Provide a state management design doc showing how conversation history and
  session context are owned and persisted.
  Provide a failure mode analysis or runbook showing how the agent behaves
  when session-service is unavailable.

----------------------------------------
EVIDENCE GAPS
  No architecture diagram or system design document showing agent boundaries
  and dependencies.
  No data contract or API schema for session-service integration (belongs to
  Foundation, not Security).
  No fallback or degradation strategy documented or implemented.
  No state ownership model defined (what is agent-owned vs. external).
  SLO document (latency, success rate, cost targets) -- blocks alerting and
  runbook prioritization
  Runbook artifacts for session-service failure, API timeout, context overflow
  -- blocks incident response
  Structured logging config (JSON formatter, session_id field) -- blocks
  production debugging
  Alerting config (CloudWatch/Datadog/PagerDuty) -- blocks real-time incident
  detection
  Eval suite with scheduled CI runs -- blocks behavior regression detection
  Postmortem template and incident records -- blocks operational learning
  No IAM policy or RBAC definition provided; cannot verify least-privilege at
  infrastructure layer
  No network security group or VPC configuration provided; cannot verify
  network isolation
  No secrets manager configuration provided; cannot verify credential rotation
  or audit
  No security scanner output (Snyk, OWASP ZAP) provided; cannot verify absence
  of other vulnerabilities
  No penetration test results provided; cannot verify real-world attack
  surface
  No timeout configuration in code or environment variables
  No retry policy or circuit breaker implementation
  No checkpoint/persistence layer or recovery mechanism
  No SLO or uptime dashboard referenced
  No chaos engineering or failure mode testing results
  No incident postmortems or reliability runbooks
  No latency dashboards (Datadog, Grafana, LangSmith) -- cannot assess p50/p95
  or trend over time
  No performance benchmarks or load tests -- no evidence of throughput or tail
  latency under realistic load
  No ADR or design doc explaining model selection rationale -- cannot verify
  if hardcoded choice is intentional
  No caching configuration (Redis, semantic cache, in-memory) -- absence is
  clear but no alternative evidence provided
  No async/concurrent patterns in codebase -- no evidence of parallelization
  attempts
  No token usage dashboard or cost trend export (LangSmith, Langfuse, Datadog
  LLM Observability) -- cannot assess historical spend patterns or anomalies.
  No budget alert configuration (AWS Budgets, Azure Cost Management, GCP
  Budget alerts) -- cannot verify alerting is wired.
  No cost tracking code or logs -- cannot verify per-session or per-turn cost
  attribution.
  No model selection ADR or decision log explaining why claude-opus-4-6 is the
  only choice
  No caching implementation or cache hit/miss metrics
  No batch processing config or async call patterns
  No cost trend data or energy/carbon reporting
  No input deduplication logic or change detection mechanism
  No eval framework or test suite (Promptfoo, Braintrust, LangSmith, or
  custom) -- blocks reasoning integrity assessment
  No reasoning trace logs or chain-of-thought output -- cannot audit how agent
  arrived at answers
  No hallucination metrics or red team results -- cannot measure failure rate
  No uncertainty quantification or confidence scoring -- cannot distinguish
  high-confidence from low-confidence responses
  No provenance audit trail for session context injection -- cannot trace
  which context fields influenced which responses
  No signal handler or kill flag implementation -- blocks external termination
  (Controllability)
  No structured logging or audit log -- blocks incident investigation
  (Controllability, Op. Excellence)
  No human-in-the-loop workflow or approval gates -- blocks compliance and
  safety (Controllability)
  No checkpoint or persistence mechanism -- blocks pause/resume
  (Controllability, Reliability)
  No timeout on external calls -- blocks cancellation (Controllability,
  Reliability)
  No context window usage dashboard or monitoring (LangSmith, Langfuse, or
  custom logging) -- affects Operational Excellence pillar
  No input validation or sanitization library (e.g., bleach, html.escape) --
  affects Security pillar
  No session state persistence layer (DB schema, ORM config) -- affects
  Reliability pillar
  No token counting integration (tiktoken config, Anthropic token API calls)
  -- affects Performance Efficiency pillar

----------------------------------------
Tokens: 62,214 in / 27,278 out
Estimated cost: $0.1589 USD
Generated: 2026-03-29 00:13
