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# Agent workflow & collaboration — guide

Reference notes, design rationale, and negative results (22) for agent workflow & collaboration. Read when working in this area.

#### 1. Design-first: write strategy/design docs before code, and never trust a 'no open questions' doc
`loop` · ⚠ _session-derived, unverified_

For a new player or substantial behavior, write per-role {ROLE}_STRATEGY.md from the rulebook/game API plus a tying DESIGN.md BEFORE coding decision logic -- forcing strategy into prose surfaces win-condition structure and asymmetries that otherwise leak in as ad-hoc code. A design doc claiming 'no open questions' is a smell: real player designs still have unresolved API, timing, identity, pathfinding, testing, and mode-composition questions. Treat 'implementation-ready' as a bar (every mode has params + behavior spec + exit conditions, every decision is a tree/table, error/recovery paths enumerated) and run the doc through an explicit issue-by-issue review pass, auditing it like code, before applying fixes.
  <sub>sources: codex:019dfe49-9c5e-76c0-86b8-99fbc2cf01b3, opencode:ses_1fbfc5c8fffesCp5Ilc2Hs3Q6X, opencode:ses_1fb8906dcffete6Fy63Vc7IEPv, opencode:ses_209613a85ffeqO1Gq9NEDobBYi</sub>

#### 2. Prefer agent-driven generation, reserve Python for glue, and build iteration tooling on demand
`loop` · **negative result** · tool: `maker_v2`

In a code-generation toolkit, prefer driving a coding-agent runner with a focused prompt plus a small validation step over hand-coded Python extraction or per-game heuristics; reserve deterministic Python for glue, schema enforcement, manifest emission, Dockerfile assembly, packaging, and tests -- not for parsing prose. A contract-first, composable, agent-driven pipeline (maker_v2) deprecated the hand-coded-deriver (maker_v1); point agent-guide generation prompts at the SAME in-repo framework (e.g. players.player_sdk) the downstream agent-maker will actually use. When a deprecated stage must serve as a temporary fallback because its successor lacks a capability, file that gap as a requirement AGAINST the successor rather than adding new features to the deprecated stage, so the deprecated path does not accrete work. Favor human-in-the-loop pipelines that turn a source tree into developer artifacts over end-to-end auto-coding, and build iteration tooling on demand as experience reveals what is needed, not up front.
  <sub>sources: players_checkouts/players/tools/cogbase/docs/designs/maker_v2_design.m, players_checkouts/players/tools/cogbase/README.md, players_checkouts/players/tools/cogbase/docs/artifact_pipeline.md, opencode:ses_223dc7136ffeCr4ZslnR426sVG</sub>

#### 3. Nightly champion loop: gate AUC>=0.70 / >=500 games, and know its silent-failure modes
`loop`

An automated nightly champion loop can run scrape -> refit -> gates (AUC>=0.70, >=500 games, full test suite, in-image smoke gate) -> vendor -> build -> upload -> submit on a cron schedule. Beware its failure modes: it silently no-ops if the machine sleeps through the scheduled time, and aborts if cloud auth expires or Docker is down. A full-corpus fit ignores recency, so add a recency window if field drift outpaces accumulation.
  <sub>sources: personal_labs/crewrift_lab/WORKING_CONTEXT.md</sub>

#### 4. Split agent-memory upkeep into capture / curate / defrag; enforce recall structurally
`generic` · **negative result**

Structure a self-improving agent memory loop as three separate on-request invocations -- capture (review one session into proposed learnings), curate (periodically prune/merge with the human), and defrag (structurally reorganize the relational map) -- not one monolithic step. Storing concept links is not enough for recall: agents won't spontaneously consult or update a concept-link index mid-conversation, so recall needs structural scaffolding that ENFORCES consultation (e.g. a separate pre-read pass that surfaces activated associations to the main agent and holds write access to keep the index current). When graduating lessons, cluster semantically (the same lesson worded differently across sessions counts as recurrence -- match on meaning, cite which sessions each cluster appeared in), check the graduation targets (best_practices files) FIRST so you don't re-promote something already practiced, and let the human make the final promote/keep-waiting/cull call rather than graduating autonomously.
  <sub>sources: metta/agent-plugins/kitchensink/skills/ks.agent-harness-design/SKILL.m, personal_labs/crewrift_lab/.claude/skills/lessons-review/SKILL.md</sub>

#### 5. Always-on prose is ignored; wire durable agent behavior into a mechanism it can't skip
`generic`

A methodology encoded as plain always-loaded CLAUDE.md prose gets filed as soft 'guidelines' that yield under task pressure and is effectively ignored. For durable behavior change, either compress it to a much shorter disposition-shifting framing, or wire it into a mechanism the agent cannot silently skip (a hook, a trigger-loaded skill, or a required ritual). Load procedural guidance on demand: factor a recurring procedure with stable mechanics into a skill (header always loaded, detail on invocation), and trigger it on a machine-detectable failure signal (e.g. a PostToolUse hook keyed off command output), since the agent cannot self-trigger on external events. Write trigger descriptions around the PHENOMENOLOGY of the moment ('when you notice yourself about to...'), not the problem category -- a category trigger is circular because it needs the very classification the skill would help make, so it fails to fire for an agent lacking the insight.
  <sub>sources: metta/agent-plugins/kitchensink/skills/ks.agent-harness-design/SKILL.m</sub>

#### 6. Skill mechanics and cross-agent distribution: scan paths, description always-on, shared plugin not symlink
`generic`

A skill is a <name>/ directory with SKILL.md (`name` + `description` frontmatter plus a markdown body); it only loads on a scan path (~/.claude/skills/ or an enabled plugin's skills/ dir), a file elsewhere is inert, and the `description` line is injected always-on as the discoverable menu entry while the body loads only on invocation -- put the always-on pointer in `description`, detail in the body. Different tools scan different dirs: Claude Code reads .claude/skills/, Codex reads .agents/skills/ (not .codex), Auggie reads both; invocation differs (/<name> for Claude Code and Auggie, $<name> or implicit description-match for Codex) -- verify against current tool docs. Keep one canonical SKILL.md and symlink to expose it elsewhere (never chain symlink-to-symlink; author cross-agent memory once in AGENTS.md and symlink CLAUDE.md to it), but a symlink only resolves on a machine with the source at the same path, so for team-wide distribution commit the REAL file into a shared plugin (e.g. kitchensink); in the metta monorepo, skills under agent-plugins/default/skills/ load via the enabled local marketplace plugin and reach teammates on next pull with no install step (the flat top-level skills dir is deprecated). A session-locator helper identifying the host agent should detect best-effort (env vars, then filesystem signals, then in-context fallback) and never raise on an unknown agent.
  <sub>sources: metta/agent-plugins/kitchensink/skills/ks.cross-agent-skill-layout/SKI</sub>

#### 7. One fact, one doc: separate DESIGN from PLAN and keep a single source of truth
`generic` · **negative result** · ⚠ _session-derived, unverified_

Guide-doc architecture that held up for an agent framework: contract-first (the formal observation/action/timing contract is the single most important doc, centralized), non-overlapping jurisdiction (every fact lives in exactly one document), and layered depth (orientation -> operational -> understanding -> architecture -> expertise) so a developer can stop at any layer and still have a working agent. Keep durable architecture (decisions, contracts, invariants) in a DESIGN doc separate from a PLAN doc holding the phased schedule/todos, and pare PLAN so it does not duplicate the architecture (the trace-event vocabulary lives authoritatively in DESIGN, PLAN points to it). Keeping both CLAUDE.md and AGENTS.md in the same role caused them to drift and produce a stale version reference (one said V9, one V10) -- unify rather than parallel-maintain.
  <sub>sources: claude-code:f886e63d-1c92-4afd-bb2b-f4ff2cd9b788, opencode:ses_206fd87a6ffel8Y8ooSpgw4Aeo, opencode:ses_206bfd18dffe1hUfJBOccuaLDC</sub>

#### 8. When independent players converge on the same shape, extract a shared framework grounded in interface contracts
`loop` · ⚠ _session-derived, unverified_

When multiple independently-built coworld players converge on the same shape, extract a shared framework rather than copy-pasting: three players (escape-room, social-deduction, resource-competition) all shared an inner perception->belief-update->decision->action loop driven by a symbolic mode-level policy plus an outer strategy loop that selects the active mode (possibly an LLM) -- that convergence is the signal worth abstracting. Expose the game-specific variable parts as interfaces (perception/frame-parsing, belief schema, the set of modes, the strategy/mode-selector) and let the framework own the fixed parts (runtime loop, mode controller, belief buffers, tracing). When shipping such a framework: ship a toy non-game example exercising the interfaces as a copyable template; leave a SOURCE_REPOS pointer file recording source repo paths; strip original internal player names from the user-facing README (keep provenance in a separate reference doc); ground the generalization in the source-code contracts (mode interfaces, belief-snapshot structs, directive schemas, buffer/channel boundaries) NOT the READMEs; and preserve any more-complete interactive chat API rundown verbatim in a 'Framework Reference' section. When one player has already solved a cross-cutting problem (e.g. a process-wide counter plus per-instance trace-dir mechanism), inspect the solved player first and port its pattern rather than reinventing it.
  <sub>sources: codex:019e18b2-4478-78d0-8882-752b7b0d55c9, codex:019e18d7-532e-7540-82ea-9abe1726aec7, codex:019e1914-2445-7fa2-af5e-8d3ca4d01c57, opencode:ses_2044035bcffeRp9M35307uA4gR (+2)</sub>

#### 9. Respect permission denials by checkpointing and surfacing to the human; a stuck sub-agent may be blocked by an earlier instance
`generic` · **negative result** · ⚠ _session-derived, unverified_

The auto-mode permission classifier blocks operations outside the declared task scope or risking irreversible loss, and these blocks are generally the right call: it blocked listing a sibling project tree as scope escalation, blocked `git stash` of the agent's own uncommitted changes as risking loss, and blocked an `rm -rf` of shared cache outside the repo (~/.nimby/pkgs/...). Keep file operations within the stated target tree, checkpoint via commit instead of stash, require explicit human confirm before mass-deleting shared cache outside the repo, and surface the need rather than working around the denial. Separately, a long-lived external subprocess agent (e.g. a `codex exec` call) can silently hang alive for 25+ minutes with near-zero CPU and no output because an earlier instance of the same binary is holding a per-user resource -- when a delegated external agent produces nothing for many minutes, kill the stuck call, check for other instances of the same binary, and retry a minimal call to confirm the environment before re-running the real one.
  <sub>sources: claude-code:1c7117ad-9bee-4658-8937-e92adbc1a6ae, claude-code:b2b2f1e5-537c-4acf-abfb-4490b0abc27a, claude-code:ca5e32ea-3dad-42a9-8991-0f07d28b1060, claude-code:cd2e0b52-b541-4e03-9fd3-798db4591603</sub>

#### 10. Convert a placeholder into a named 'default' rather than keeping template plus default
`crewrift` · ⚠ _session-derived, unverified_

When asked for a 'default' artifact, James preferred converting the empty graders/templates/grader_template/ placeholder INTO a named 'default' grader (a simple generic interestingness scorer usable when a world has no game-specific grader) rather than keeping a separate template plus a default. Prefer collapsing a placeholder-plus-default pair into one named, usable thing.
  <sub>sources: codex:019e66c3-638e-7e70-afdf-8f6df9299f6a</sub>

#### 11. Split the agent into a fast symbolic inner loop and a slow LLM outer loop emitting durable intents; gate stale directives
`loop` · ⚠ _session-derived, unverified_

Split an agent into a fast per-tick symbolic inner loop and an async LLM outer loop, where the outer loop only selects the active 'mode' (durable strategic intent) and the inner loop reactively selects tactical tasks each tick. Design outer-loop decisions to age gracefully: an LLM call taking 1-2s means its belief snapshot may be 24-48+ ticks stale by the time the directive lands, so the outer loop emits DURABLE intents and the inner loop handles tactical correction reactively. Do not apply stale outer-loop directives unconditionally -- gate them on tick/phase/view preconditions and add staleness/rejection rules, because in short rounds (e.g. 15s) a 1-2s latency is strategically large and an old-state directive can be actively harmful. Do not throttle the outer loop with per-tick sleeps (a 50ms sleep was removed) and keep an explicit mode directive crossing the outer-to-inner boundary rather than having the inner loop infer mode from shared memory. Make tasks coarse-grained multi-frame behaviors so the LLM never reasons about frame-level input: each task (explore, move_to, pursue_player, open_chatroom, offer/accept_role_exchange, shout) encapsulates a behavior with an act(perception, belief) -> FrameAction method and owns its button sequencing -- this task seam is what makes the latency decoupling work. Allow a deliberate, documented exception where a task must block the fast loop (a chat_and_observe task makes synchronous LLM calls to compose chat text, intentionally blocking because the agent cannot meaningfully act while writing).
  <sub>sources: claude-code:27984094-3b9e-4e67-92c5-da2a61ceb32a, claude-code:fa645b7b-4fa0-4b55-992e-b273ee703391, codex:019dfe49-9c5e-76c0-86b8-99fbc2cf01b3, codex:019e1914-2445-7fa2-af5e-8d3ca4d01c57 (+1)</sub>

#### 12. Isolate the LLM outer loop behind buffers, validation, snapshots, and a no-op default
`loop`

Connect an async LLM outer loop to the inner loop via two size-1 consume-on-read (latest-wins) buffers so the inner loop never blocks; if the outer loop is slow, crashes, or absent, the inner loop keeps running and a watchdog activates an agent-defined fallback mode after ticks-since-last-directive exceeds a threshold (default 120 ticks ~= 5s), restarting the thread on crash. Restrict the outer loop's only write channel to a dedicated 'inferences' namespace replaced wholesale each cycle, keep framework-managed fields read-only to it, and validate mode directives at CONSUMPTION time in the inner loop (mode in registry, params isinstance the declared type) -- on invalid, log and keep the current mode. Snapshot (deep-copy) belief state before each agent hook and roll back if it raises, then continue with the next hook (hooks must not assume prior hooks succeeded). Gate all LLM runtime control behind a deterministic validation layer with a closed semantic decision schema, a JSON-safe context, an offline shadow runner, and a no-op 'hold' provider as default, so the agent is fully runnable and testable without any model calls.
  <sub>sources: orpheus/DESIGN.md (Orpheus agent framework design), players_checkouts/players/users/james/personal_cogs/persephone/README.</sub>

#### 13. Orpheus inner loop: phase-agnostic, perception-inferred completion, view-gated tasks
`loop`

Run the per-tick pipeline (perception, belief-update, decide, act) in the same order every tick regardless of game phase, with no framework-level phase gating -- phase-appropriate behavior emerges from modes returning idle/no-op tasks, because sending an empty button mask in a non-interactive phase is harmless. Give every task a valid_views set; before calling a task the framework checks belief_state.view against it and emits a no-op on mismatch. Do not let tasks self-report completion: a task can only assert it sent a command, not that the world changed -- treat completion as a belief-level concept inferred from perception, and if an action's effect is undetectable, fix the perception gap rather than adding a completion-signal channel. Separate per-task execution state (ActionMemory: path waypoints, commands sent, ticks active) from durable belief state and clear it only when task identity (type + params) changes, so re-affirming the same task preserves in-progress execution.
  <sub>sources: orpheus/DESIGN.md (Orpheus agent framework design)</sub>

#### 14. Build a per-tick game policy as a crash-safe four-stage deterministic pipeline
`loop`

Structure a scripted per-agent policy as perceive (parse sparse observation tokens into a delta, write seen cells to sparse spatial memory) -> interpret (run to fixpoint deriving higher-order beliefs) -> decide (select exactly one validated skill) -> handle_failure (emit a safe no-op if any stage raised). Wrap EACH stage in its own try/except (not one big one) so an exception never kills the episode and the trace gets per-stage failure categories (PERCEIVE_RAISED, INTERPRET_RAISED, DECIDE_RAISED, SKILL_NOT_FOUND, PRECONDITION_FALSE, ARG_VALIDATION_FAILED, SKILL_RAISED, SKILL_RETURNED_INVALID) telling an offline optimizer exactly where to look. The handler is itself improvable, but wrap it in one final try/except emitting a hardcoded Action(name='noop') with no Python in the loop -- this last-resort fallback is structurally guaranteed not to fail and is intentionally NOT improvable. Iterate Interpret to a fixpoint by hashing canonical-form JSON working memory before/after each pass, stopping on a match or at MAX_INTERPRET_ITERS (cap 8, hitting it is a defect signal to log); never retry a failed stage within the same tick because rerunning corrupted state compounds the failure.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/agent-architecture.md, archive/cogames_playground/alpha_cog/docs/paper/paper.md, archive/cogames_playground/alpha_cog/docs/cogs-v-clips-strategy.md</sub>

#### 15. Measure per-tick latency don't enforce it; cap working memory; keep skills reactive
`loop`

Do not use hard timeouts to enforce a per-tick latency budget in a synchronous Python policy: OS signals are unix-only and not thread-safe, and subprocesses are too heavy for a ~10ms budget. Measure wall time per stage with time.perf_counter() and treat budget violations as an offline optimizer signal, not a runtime intervention. Cap serialized JSON working memory (e.g. 128 KB) and log loudly on violation -- the cap exists to surface bugs (an interpret rule appending every pass), not save storage. Forbid skills from calling each other directly; encode composition as a sequence/intent in working memory and re-decide each tick, keeping the agent reactive over a short horizon (matching partial observability). Size skills at role-logic granularity (move_to_coord, pick_up_nearest_heart, flee_from_threat) -- smaller adds decision overhead, larger is hard for an LLM to rewrite -- and expect to refactor the first pass at least once, so start small.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/agent-architecture.md</sub>

#### 16. Use ring buffers for exact windowed queries, EMAs only for approximate trends
`loop`

For temporal reasoning in a per-tick policy, use a hybrid memory layer: ring buffers where you need precise windowed queries (e.g. minimum distance-to-target over the last N ticks for stuck detection) and exponential moving averages where approximate trends suffice (deficit fraction, deposit-throughput rate). EMAs are O(1) memory per signal but lack precise window semantics, so do not use them where you need an exact 'fraction of last N ticks' answer.
  <sub>sources: archive/cogames_playground/alpha_cog/docs/temporal-memory-design.md</sub>

#### 17. Decompose a large bot state object into named, single-owner sub-records
`generic`

Decompose a ~80-field bot/agent state object into ~10 named sub-records (Perception, Motion, Tasks, Goal, Identity, Evidence, ImposterState, VotingState, ChatState, Paths, RngStreams), each owned by one module, with leaf procedures taking explicit sub-records and only orchestrators taking the whole mutable object. Keep the module import graph a strict lower-to-higher tree. This localizes ownership and avoids 4000+-line monoliths, making behavior easier to read, test, and trace. Route new world-knowledge into the matching memory module rather than into the policy directly (e.g. Bulbacog's bulbacog/memory/ splits into spatial.py with an 88x88 map and A* pathfinding, declarative.py for beliefs/facts/peers, temporal.py for ring buffers + trends, unified behind memory.py, with compound.py holding bootstrap geometry).
  <sub>sources: bitworld/among_them/players/mod_talks/DESIGN.md, bitworld/among_them/players/modulabot/DESIGN.md, opencode:ses_224322057ffexj7SYlcfVA0zgy, opencode:ses_1f6fd3cbeffesP62asFzIp32PQ (+1)</sub>

#### 18. Coordinate multi-agent teams deterministically through in-game channels only
`loop`

Enforce strict per-agent isolation: each agent gets its own working/spatial memory with no shared blackboard or out-of-band channel, so coordination flows only through in-game communication actions available in tournament play -- avoiding private infrastructure you must rip out before submission. Derive each agent's opening role/resource/direction deterministically from its own slot id (visible to nearby teammates) so every agent independently reaches the same assignment with no contention, and teammates running other policies cannot break it. With two channels, use the persistent one-byte 'vibe'/role signal as the cheap identity channel emitted once per role transition, and reserve the text/talk channel for news the receiver cannot infer (resource sightings, extractor depletion, death locations).
  <sub>sources: archive/cogames_playground/alpha_cog/docs/agent-architecture.md, archive/cogames_playground/bulbacog/designs/STRATEGY_V2.md</sub>

#### 19. Build screen-reading game players in capability order: see before you strategize
`loop`

Build a screen-reading player perception-first, in capability order: read the URL and show frames, unpack frames, send/verify input masks, add a debug viewer, decide what geometry to vendor, package linux/amd64, THEN add interstitial detection, localization with dynamic-pixel masking, temporal+spiral search, task-icon detection, radar, momentum-aware A*, task hold, body reporting, voting, imposter and ghost behavior, and only then upload and profile. Structure per-frame logic as a linear pipeline (connect, receive, unpack, tick, detect interstitial, parse/update state, localize camera, update tasks, choose goal, choose input mask, send, draw debug) rather than callbacks, because a pipeline is far easier to debug. Do not start with clever strategy; start with seeing, localizing, and drawing what the bot believes.
  <sub>sources: bitworld/among_them/players/how_to_make_a_bot.md</sub>

#### 20. Stage agent-framework builds bottom-up; auto-discover agents by convention
`loop`

Stage agent-framework implementation so each layer builds on a tested lower one: core data types/interfaces first, then a no-op end-to-end pipeline skeleton, then belief update, spatial/pathfinding, tasks, hooks, mode switching, outer loop, logging, and finally a test agent for integration. Auto-discover agents by convention (agents/*/policy.py) with a simple per-agent contract: runnable as a script accepting --url and --name, plays until disconnected, exits cleanly on SIGINT/SIGTERM, and exposes optional module-level metadata constants. Structure a risky multi-phase build as logically mergeable phases (e.g. scaffold, perception port, crewmate, meetings/voting, imposter) with a stop-and-review and explicit done-when criteria at each boundary, broken into Graphite stack sub-commits ordered by dependency. Keep a deliberately minimal 'tiny baseline' policy (single-file ~250-line DemoPolicy: mine one heart-recipe resource, deposit, wander from walls when idle; no roles/gear/coordination) as a readable reference newcomers read first.
  <sub>sources: orpheus/IMPLEMENTATION_PLAN.md (Orpheus implementation plan), players_checkouts/players/users/james/personal_cogs/persephone/README., players_checkouts/players/archive/players/among_them/coborg/PLAN.md, players_checkouts/players/players/cogsguard/tiny_baseline/README.md</sub>

#### 21. Name modes/states by what the code does; don't abstract before a second case exists
`generic` · **negative result**

Choose mode/state names by what the code actually does, not by intent: collapsing aligner behavior into a single 'PatrollingMode' was the wrong split because the mode was really doing outward expansion, so trace review separated ExpandingMode (outward quadrant sweep with local capture and restocking) from FrontierPatrollingMode (boundary patrol, neutral repair, paired scrambler-raid staging), with mode-stability hysteresis to prevent churn. Do not introduce a policy registry or extract a policy object before a second policy actually needs it; keep boolean strategy flags (e.g. aggressive_imposter) as injected constructor config and abstract only when a real second case arrives. Cancel a planned mechanical rename (e.g. an FFI symbol prefix change) when it is high-churn but produces no behavior change -- keeping a legacy prefix costs nothing and avoids needless diff noise. The same logical constant can legitimately live in two layers with two purposes (a kill-strike range as a local action-layer button-press threshold AND an exported tuning constant for the hunting mode's kill-confirmation check); documenting them as one constant hides the coupling.
  <sub>sources: archive/cogames_playground/bulbacog/designs/ALIGNER_SPIRAL.md, players_checkouts/players_2/players/crewrift/crewborg/docs/designs/con, bitworld/among_them/players/mod_talks/DESIGN.md, opencode:ses_206fd87a6ffel8Y8ooSpgw4Aeo</sub>

#### 22. Build asymmetric crewmate/imposter policies over one shared perception stack
`crewrift`

For a social-deduction game (e.g. Among Them), keep two distinct policies (crewmate vs imposter) sharing one perception stack and build deliberate asymmetry: the crewmate is principled and quiet (pure task throughput, reports only stumbled-on bodies, votes only on firsthand evidence) while the imposter is a theatrical social engineer. The asymmetry is the point -- it makes the crewmate a hard target and the imposter a manipulator. Treat a perception/navigation reference written in another language (e.g. a Nim bot for a Go player) as a behavioral reference to re-derive idiomatically, not as source to port line-for-line.
  <sub>sources: bitworld/among_them/players/evidencebot_strategy.md, bitworld/among_them/players/lively_lecun/ROADMAP.md</sub>
