`fs-deploy-agentops`¶

Package and deploy the consumer agent to Teradata AI Studio's AgentOps platform — preflight, generate, zip, deploy, smoke-test.

name: "Feature Store: Deploy to AgentOps" description: "Package and deploy the tdfs4ds consumer agent to Teradata AI Studio's AgentOps platform — preflight the configuration, generate agent.py/customer_agent.py/config.yaml/requirements.txt from the running session, build the deployable zip, and smoke-test the live agent. Invoke when the user wants the feature-store chatbot running as a hosted AI Studio service rather than a local Gradio app. SKIP when the user wants to run the agent locally — use fs-agent." argument-hint: "[agent-directory][mcp-enabled: true|false]" allowed-tools: "Read, Edit, Write, Bash, Glob, Grep"

Generate the deployment package that runs the tdfs4ds consumer agent as a hosted service on Teradata AI Studio's AgentOps platform, then verify the deployed agent actually works.

User context: $ARGUMENTS

Step	Skill	Why
Before	`fs-setup`	The package is derived from a working session — connect and `setup()` first.
Before	`fs-document`	The agent answers only from the business dictionary. An undocumented store deploys a mute agent, and the preflight fails on exactly this.
Before	`fs-vector-store`	Prefer the Teradata backend for a pod: a Chroma index lives on the container filesystem and is rebuilt on every restart.
Before	`fs-agent`	Run the agent locally first. If it cannot answer here, containerising it will not help.
After	`fs-inspect`	If the deployed agent finds nothing, confirm what is actually registered.

The platform contract¶

AgentOps deploys from a zip holding four files plus its own agent_runtime/ library:

File	Owner
`agent.py`	generated — platform entry point
`customer_agent.py`	generated — agent logic
`config.yaml`	generated — non-secret settings, committed
`requirements.txt`	generated — pip dependencies
`agent_runtime/`	the platform's SDK — generated by tdfs4ds when absent; a platform copy is never overwritten

agent_runtime is on no package index — the PyPI names agentops and agent_runtime are unrelated third-party projects, and installing either produces a green build then an ImportError at startup. Never put them in requirements.txt.

By default (agent_runtime='auto') tdfs4ds generates a compatibility implementation of the two-symbol contract when the destination has none, so no scaffold from demo_agents/ is needed. If a platform-supplied copy is present it always wins — its host may rely on details beyond the public surface. Use agent_runtime='require' to insist on the platform's own.

One call (preferred)¶

report = tdfs4ds.deploy_to_agentops(
    './teradata_agent',
    database='MY_FEATURE_STORE',      # REQUIRED - stated, never inherited
    databases_readonly=['LAB_STORE'], # optional, each validated; omitted = none (0.3.6.9)
    data_domain='CLAIMS',
    token=user_token, auth='jwt', mcp=False,
    progress=print)

Seven stages: validate target -> preflight -> publication gate -> package+zip -> register -> deploy -> verify. A failing stage stops the pipeline (registering a definition whose preflight failed only creates registry litter and hides the cause). dry_run=True stops after the zip and returns the payload without touching the API — always run it first.

report['insights'] carries the pod-log findings, analysed whether or not the deploy succeeded.

Tracing (0.3.2.0). The package traces each turn with Opik: config.yaml sets TDFS4DS_TRACING: "opik" and requirements.txt installs opik. Each turn gets a span per graph node and per LLM/tool call, grouped by session_id. The Opik SDK reads the platform-injected OPIK_* settings. Its own analytics and Sentry reports stay off (TDFS4DS_OPIK_VENDOR_TELEMETRY: "false"). Set tdfs4ds.TRACING = 'off' before packaging to deploy without it. Not yet observed on a real pod: say so if asked whether traces reach the AI Studio Opik.

database= is required. The feature store is a deployment target, not ambient state: without this the pod silently inherits whatever the session had configured, which is invisible at the call site. Model / embedding / Vector Store settings still come from the running session.

Deploy with mcp=False first. It removes Enterprise auth and the 132-tool context budget from the run, so any failure is unambiguously about the agent. Only two intents are omitted (DATA_QUERY, ALGORITHM); every other skill works.

Find, call and remove a deployed agent by name¶

url = tdfs4ds.agent_endpoint("tdfs4ds-propensity-demo", token=user_token)          # base URL
inv = tdfs4ds.agent_endpoint("tdfs4ds-propensity-demo", invoke=True, token=user_token)  # POST here

tdfs4ds.remove_agent("tdfs4ds-propensity-demo", token=user_token, dry_run=True)     # what would go
tdfs4ds.remove_agent("tdfs4ds-propensity-demo", token=user_token)   # deployment, then definition

The agent is named by deployment name, UUID, or the short id the Playground shows (85f752e). A name reused by a terminated deployment resolves to the live one; several live matches are refused with the candidates listed.
GET /agents lists the whole workspace, so a name can be another user's agent: remove_agent refuses a deployment whose created_by is not the token's user unless others=True.
The definition is deleted too (delete_definition=True) unless another live deployment uses it.
A name can be deployed only once: before re-deploying under the same name, remove the earlier deployment (tdfs4ds.agentops.find_agent(name) raises LookupError when there is none).

Reading the pod log¶

report['insights'] (or tdfs4ds.analyze_pod_log(text)) matches known signatures:

Signature	Means
`pip_unresolvable`	the version is not on the pod's index — the build never reached your code
`agent_runtime_missing`	the platform library is in neither the zip nor the base image
`no_credentials`	`runtime_config` did not arrive, or the wrong auth mode
`token_rejected`	the JWT expired, or the caller cannot log on
`schema_missing`	the named store is not readable by the identity the pod connects as
`structured_output`	the gateway relays `response_format` without enforcing it
`oom`	resources too small
`startup_ok`	positive — the agent reached its own configuration check

An empty result is not a healthy start: look for startup_ok to confirm one.

Step-by-step (when you need the pieces)¶

import tdfs4ds

# 1. Preflight — in the session that already answers questions correctly.
#    Folds in check_configuration(), then adds the deployment-specific checks.
tdfs4ds.check_agent_deployment(dest='./teradata_agent')

# 2. Inspect before writing anything (the repo's generate/write idiom).
result = tdfs4ds.generate_agentops_package(mcp_enabled=False)
print(result['files']['customer_agent.py'])
print(result['files']['config.yaml'])

# 3. Write into the scaffolded directory + build the zip.
result = tdfs4ds.write_agentops_package('./teradata_agent', mcp_enabled=False)
print(result['zip_path'])          # -> ./teradata_agent.zip
print(result['runtime_config'])    # -> goes in the deployment payload, NOT on disk

Then run notebook 02's upload / poll / deploy cells unchanged, using the generated payload:

payload = tdfs4ds.deployment_payload(agent_definition_id, result['runtime_config'])

Finally, verify the live agent:

tdfs4ds.check_deployed_agent(agent_url, token=user_token)

Authentication: `auth='service'` vs `auth='jwt'`¶

Pick before generating; it changes the agent, not just a setting.

Mode	When	How the pod connects
`service` (default)	You have a Teradata username/password for a service account	One long-lived context opened at import from `TDFS4DS_TD_*`. All callers read as that user.
`jwt`	The platform authenticates each caller with a token and there is no password	No connection at import. Each caller's `auth_context.token` opens the session, rebuilt when the caller changes.

The pod's token is NOT %gen_token's, and that distinction is the whole risk. AI Studio issues two tokens that look alike: the session/SSO token — which is what reaches the pod as auth_context.token — and %gen_token's user_token, minted for a Vantage logon. Confirmed live: the first is refused by the database with Error 8017 / SQLState 28000, the second is accepted, and they are different strings. A pod cannot call %gen_token, so auth='jwt' works only where the platform's own token is also accepted as a database logon — a Vantage-side trust setting for the token's issuer, not anything the deployment configures. Verify before deploying (§ below); where it is refused, auth='service' is the way in.

Under jwt, three consequences follow from the token model and are baked into the generated code: the bootstrap is lazy (no credential exists at import — tokens arrive per request); the rebuild is keyed on the token (tokens expire, and the next call brings a fresh one, so it self-heals); and the context is rebuilt per caller. That last is only safe because turns are already serialized — teradataml keeps one process-wide context, so interleaved turns would run under the wrong identity. connect() still runs once per process: catalog views belong to the store, not the caller.

Costs of jwt, state them to the user: every caller needs SELECT rights on the feature store (there is no service account behind it), and switching caller costs a reconnect. Only a fingerprint of the token is kept, never the token.

tdfs4ds.check_agent_deployment(auth='jwt')
result = tdfs4ds.write_agentops_package('./teradata_agent', auth='jwt')

Under jwt only TDFS4DS_TD_HOST is required at deploy time.

`TDFS4DS_TD_HOST` in a pod: use the IP address¶

Verified on a live AgentOps deployment: dbc does not resolve from inside the pod, and neither did any qualified form - DNS for the database was unavailable there entirely. The fix was to set TDFS4DS_TD_HOST to the database's IP address.

This matters because the notebook is not evidence. A bare name like dbc is completed by the resolver's search domains, and an AgentOps pod need not share the notebook's - so the same value works in one and fails in the other, and the failure carries no hint that a name was the problem:

[ERROR] reconnect_teradata: reconnecting to Teradata host=dbc user=DM5344095
[ERROR] reconnect_teradata: reconnection failed - (TDML_2000) Failed to connect to Teradata Vantage.

Read the timing. Those two lines are 88 ms apart. TDML_2000 is identical for three different faults, and the elapsed time separates them:

Elapsed	Meaning	Fix
well under a second	the name never resolved	the IP address, or a qualified name that resolves
seconds, then failure	resolved but unreachable - routing or firewall	a platform question, not a setting
a round trip, `SQLSTATE 28000`	reached and refused	the credential (`auth=` mode, password, token audience)

Since v0.3.1.184 the generated agent prints this diagnosis itself on the failure path - the resolved addresses or the resolver's error, the resolver's search domains, and a TCP probe of port 1025 when a name does resolve. Read it before changing anything.

Two consequences of using an IP, state both to the user: it pins the deployment to an address that can change, so re-check it if a working pod later stops connecting; and it is unrelated to the auth mode - the same host failure appears under service and jwt alike, so a connection failure is never evidence about the credential.

Also: Runtime: X | Mode: N/A in the pod banner is a consequence of this under auth='service', not a second problem. That path connects at import, so a host failure is fatal and the runtime never constructs - the pod serves /health while the agent does not exist, which the platform reports as unhealthy. Under jwt the identical fault appears per request instead, because that path connects lazily.

And JWT Auth: ... | Issuer: ... / Authorization: disabled in that banner describe who may call the agent's HTTP endpoint - inbound platform auth. They say nothing about the Teradata connection; changing them cannot affect TDML_2000.

Where each setting goes — and why¶

The split is derived from tdfs4ds.config's own credential classification, never guessed:

Destination	Contents	Rationale
`config.yaml`	ordinary settings (`SCHEMA`, `DATA_DOMAIN`, model names, `AGENT_ANSWER_STYLE`…)	bundled in the zip and committed — must be safe to read
`runtime_config`	credentials, `TDFS4DS_TD_`, `TDFS4DS_VECTOR_STORE_AUTH_`, `MCP_USERNAME`, `MCP_AUTH`	injected at deploy time; connection vars identify a deployment, so committing them pins the package to the environment it was built in
dropped	`TDFS4DS_SERVE_`, and `CHROMA_` on the Teradata backend	the platform is the server; a Chroma path in a pod is misleading

config.yaml lists the omitted variable names as comments, so whoever reads it can tell what the deployment must supply.

MCP_USERNAME and MCP_AUTH are named individually because neither is a secret by name and neither carries a protected prefix. MCP_USERNAME is half a credential pair -- its partner MCP_PASSWORD is already a credential, and MCP_USERNAME falls back to TDFS4DS_TD_USERNAME, so a committed config.yaml would otherwise carry the database username in clear while the password travelled separately. MCP_AUTH is an auth-mode override (build_mcp_headers resolves Basic from it before the edition), so a committed file could change how the pod authenticates. Under mcp=False both are dropped anyway, so the default deployment is unaffected. They are deliberately not added to config._is_secret: that is the package-wide authority on "is this a credential?", and neither is one -- "may this be committed?" is a different question, which _DEPLOYMENT_VARS answers.

runtime_config is not a vault - do not describe it as one. Its guarantee is "not committed to source control", which is weaker than confidential: the values are stored in the deployment record and displayed in clear in the AgentOps UI to anyone who can view the deployment. State this to the user, because a deploy effectively publishes those credentials. The destination still matters -- git history is permanent and widely readable, while a deployment record can be deleted and its credentials rotated -- but the difference is durability and blast radius, not secrecy.

Deployment constraints — not preferences¶

The generated deployment_payload() differs from the tutorial's in four places, each load-bearing:

Setting	Value	Why
`resources`	`2Gi` / `1000m`	`512Mi` does not hold teradataml + langchain + pandas + networkx
`replicas`	`1`	conversation memory is an in-process `MemorySaver`; a second replica answers with no history
`enable_memory`	`False`	the platform's PostgreSQL checkpointer cannot be injected into the tdfs4ds graph, so `True` provisions a database nothing reads
turns	serialized	`graph._with_domain` mutates the module-global `DATA_DOMAIN`; concurrent turns in one pod corrupt each other. Enforced by an `asyncio.Lock` in the generated code

Consequence to state plainly to the user: session memory works for as long as the pod lives and is lost on restart. That is a known limit of this packaging, not a misconfiguration. Lifting it needs a checkpointer= passthrough in tdfs4ds/agent/graph.py.

Known blockers, in the order they bite¶

The version must be installable from the pod's index. A local-only build imports fine here and fails pip install there. The pip_resolvable check catches it; the fix is to publish, or vendor a wheel into the agent directory.
STRUCTURED_OUTPUT_METHOD unset. An in-cluster LiteLLM gateway commonly relays response_format without enforcing it, and the default json_schema path then dies on the first intent classification. Pin it. Do not guess the value: check_configuration() probes the endpoint and names it, and check_configuration(pin=True) records it in the .env stamped with the endpoint and date. Note that a pinned value travels into the pod's config.yaml, so re-probe if the pod uses a different gateway.
auth='jwt' where the environment allows it. The token arrives per request so expiry self-heals, the pod connects as the caller, and no durable password sits in runtime_config (which renders in clear in the deployment UI). But it is conditional: the platform's auth_context.token must be accepted by Vantage as a JWT logon, and a token minted for a platform API is not automatically a database credential — confirmed refused on at least one site. Settle it from the notebook before deploying, with the token the pod will see (OAUTH_ACCESS_TOKEN, not %gen_token's):
```
import os, teradatasql
teradatasql.connect(host='<db-ip>', logmech='JWT',
                    logdata='token=' + os.environ['OAUTH_ACCESS_TOKEN']).close()
```
Raw teradatasql, not tdml.create_context: teradataml holds one process-wide context, so testing with it would replace the working connection in your own notebook. Error 8017 there means auth='jwt' is not deployable on that site. 4. No agent_runtime/. The definition registers happily and the pod then fails at startup. 5. Empty business dictionary. The agent deploys and answers nothing.

Reading the smoke test¶

check_deployed_agent sends three turns — an opening question, a follow-up on the same session_id, and the same follow-up on a fresh one:

Check	Failing means
`reachable`	401/403 = expired JWT (re-run `%gen_token`); 404 = wrong URL; 5xx = the pod started then failed — read `GET /agents/{agent_id}/logs`
`grounded`	the pod answered without reaching the feature store — compare its startup `check_configuration` block against this session's `SCHEMA` / `DATA_DOMAIN`
`session_memory`	`session_id` never reached the graph as `thread_id`, or `replicas` > 1
`session_isolation`	conversations are shared between callers — answers can leak between users. The thread id has degraded to a constant

session_memory failing while session_isolation passes isolates the fault to session_id plumbing; both failing means the thread id is constant.

Do not¶

Do not hand-edit the generated files. Regenerate, so the deployment keeps matching the session it was built from.
Do not put credentials in config.yaml — it is committed, permanently. runtime_config is the right destination, but tell the user it is readable in the deployment UI rather than implying it is secret.
Do not raise replicas to scale. Scale with separate deployments until the graph accepts an external checkpointer.
Do not set streaming=True. consumer_agent_stream is a synchronous generator; bridging it to the platform's SSE format is not a passthrough.

fs-deploy-agentops¶

Related skills (call before / after this one)¶