Agentic rollout

Work in stages. Identify the user's current stage from the request and available artifacts before reading or changing code.

Stage router

Start every response with the current stage letter and name, evidence available, and blocking unknowns. Do not repeat a completed stage when its evidence is still current; recheck drift-prone configuration and runtime state.

Non-negotiable boundaries

• Inspect the current checkout, diff, and exact source before making claims.
• Treat model-facing messages, tools, and chat_template_kwargs as the context contract. Python calls and internal agent objects are not substitutes.
• Treat reasoning and tool-call parsers as part of the model/chat-template contract. A basic text response does not prove parser compatibility.
• Distinguish logical prompt Groups, resident Sessions, model requests, exported contexts, and physical training rows.
• Apply export, advantage, and dynamic-batching rules to exported contexts per Session. The number of resident Sessions is a different dimension.
• Choosing resident Agentic rollout (--use-agentic-rollout) gives Agentic ownership of train and Eval generation; --custom-generate-function-path is ignored on this path.
• Distinguish the timeout of each Relax-facing model API request from --agent-timeout. The former must let one request survive prelaunch, partial, or fully-async holds; the latter is a Runtime active-time safety budget intended to contain a stuck agent loop or tool execution.
• Identify whether the agent uses Chat Completions, OpenAI Responses, or Anthropic Messages, and compare its payload with the supported fields, Items, and blocks. Record the request's stream setting and require complete history on every request. Use runtime-operations.md for transport-specific response checks.
• Inspect fixed internal widths only when the large-scale risk gate is triggered.
• Keep this skill Agentic-specific. Route RL algorithms, OPD/TIS/loss details, generic TransferQueue sampling, model backends, and Ray scheduling to their dedicated experts or skills after checking the Agentic boundary.
• Never assume a remote agent platform has enough slots. Unknown capacity produces an UNVERIFIED Stage C result.
• Do not launch a remote experiment unless the user explicitly requests it. Load relax-dev-debug for code changes or remote validation.
• Respect explicit edit and test boundaries. Validation does not authorize adding tests or changing unrelated files.

Stage A: Assess the Agent

Goal: decide whether the existing agent can connect without corrupting context, export, or execution semantics.

1. Read the agent entry point, Relax-facing agent client, its request and outer task timeouts, tool loop, context store, compaction/retry logic, and final-output path.
2. Capture or reconstruct exact model request and response payloads. Prefer the HTTP payload over internal message classes. Record the endpoint, protocol, stream value, authentication header, complete-history representation, reasoning/tool blocks, and missing, null, or zero-length content. Resolve the final request URL after client-specific path handling; determine whether the client appends its resource path or constructs the endpoint itself, and verify that the Relax route prefix is preserved.
3. Determine the raw reasoning and tool-call syntax produced by the model under the exact chat template. Identify the compatible SGLang parser names or prove that no parser is required.
4. Read context-linearity.md and classify every transition as append, intentional fork, accidental fork, or reset.
5. Read integration-contract.md and compare the agent with the Relax input, API, and output contracts.
6. Read runtime-operations.md and check whether the agent client relies on unsupported API or process behavior.
7. Determine whether execution is local per Session or submitted to a centralized remote platform. Record any train/Eval slot pools and hard concurrency limits.

Return:

Current stage: A
Stage name: Assess the Agent
Integration readiness: READY | NEEDS_CHANGES | BLOCKED
Context topology: linear | intentional branches | accidental branches | unknown
Execution shape: local | remote shared | remote dedicated | unknown
Required adaptations:
Blocking unknowns:
Next allowed stage:

Do not modify Relax or start an experiment in this stage.

Stage B: Bridge into Relax

Enter only when the user asks for implementation and Stage A has no unresolved blocker.

1. Preserve the agent's normal model-and-tool loop.
2. Map dataset fields with --input-key, --metadata-key, and --multimodal-keys; decide whether the harness receives ready-to-use messages or constructs them from metadata at runtime.
3. Wire the complete agent environment in integration-contract.md.
4. Configure the agent application's client that calls RELAX_BASE_URL so the timeout of each model API request can span every applicable prelaunch, partial-rollout, or fully-async hold. Keep this client timeout separate from --agent-timeout, which bounds the agent process's Runtime active time.
5. Send complete histories on every model request: Chat uses messages, Responses uses typed input Items, and Messages uses system + messages. Keep tools and applicable template arguments stable. Do not use Responses previous_response_id as a continuation mechanism.
6. When canonicalization is needed, ask before changing developer semantics, choose a stable nonempty representation for empty tool results, and reuse the canonical payload in later turns and explicit export.
7. Configure --agentic-reasoning-parser and --agentic-tool-call-parser when required by the verified model/template format. Do not copy parser names from a different model recipe.
8. Use agentic-training-contract.md for export, advantage, logical identity, and dynamic-batching decisions. Nonlinear history requires explicit export. Custom advantage uses named explicit export records. Explicit export remains canonical Chat-shaped messages, nested function tools, and chat_template_kwargs regardless of the request protocol. Custom advantage returns None or a list with one result mapping per sampled Session. In each mapping, an export's advantage is a scalar shared by all assistant turns or a list with one value per assistant turn. Keep every normalization input in export metadata.
9. Decide where reward and advantages come from. For one implicitly exported context, write reward in the agent output or configure --custom-rm-path. When using --agentic-custom-advantage-path, use explicit export and keep its inputs in export metadata. Multiple exported contexts require custom advantage and dynamic batching.
10. Keep the change at the adapter and recipe boundary unless the verified contract requires a core relax/agentic/** change.

Start with:

• docs/en/guide/agentic-rollout.md
• the closest maintained example, such as examples/mini_swe_agent/
• integration-contract.md

Read the exact core source only when a verified incompatibility or requested core change requires it.

Stage C: Check before Launch

Goal: produce a launch verdict without starting the job.

1. Re-read the final agent adapter and launch script.
2. Read parameter-preflight.md. Resolve every applicable Agentic parameter after defaults and validation, then check its dependencies and runtime evidence.
3. Audit context linearity again from the integrated HTTP payloads.
4. Read resident-lifecycle.md. Verify Group size, first-request barrier, and cleanup.
5. When agents use a capacity-limited external platform, read external-agent-capacity.md. Otherwise report External capacity: N/A.
6. Read large-scale-rollout.md if and only if the projected or observed load is large enough to approach or exceed a fixed internal width or known validated scale envelope. Otherwise report Internal scale: N/A; prelaunch, production use, or a "large-scale" label alone does not activate this check.
7. When the agent uses explicit export, nonlinear history, multiple contexts, custom advantage, --log-passrate, a reward object or --reward-key, a configured RM, or --group-rm, read agentic-training-contract.md.
8. Read runtime-operations.md. Verify protocol and transport compatibility, the per-request Relax-facing client timeout, reasoning/tool-call parsers, the agent process timeout, optional KV/admission flags, errors, and observable evidence.
9. When prelaunch, partial rollout, or fully async is enabled, read partial-and-async-lifecycle.md and verify the applicable cross-step state transitions.
10. Report unknown remote capacity, internal scale evidence, or networking as blockers rather than optimistic assumptions.

Return:

Current stage: C
Stage name: Check before Launch
Verdict: PASS | UNSAFE | UNVERIFIED
Agentic parameters: PASS | NEEDS_CHANGES | UNVERIFIED
Context linearity: PASS | NEEDS_CHANGES | UNVERIFIED
External capacity: PASS | UNSAFE | UNVERIFIED
Internal scale: PASS | UNSAFE | UNVERIFIED | N/A
Internal width changes: N/A | see conditional large-scale result
Relax-facing per-request timeout: PASS | UNSAFE | UNVERIFIED
Reasoning/tool-call parsers: PASS | NEEDS_CHANGES | UNVERIFIED | N/A
Export/advantage/batching: PASS | NEEDS_CHANGES | UNVERIFIED
Required slots:
Configured slots:
Blocking items:
Ready to launch: yes | no

Stage D: Demonstrate with an Experiment

Enter only after explicit user authorization and a passing Stage C check. Use the environment-specific launch skill when one exists; otherwise follow relax-dev-debug and ssh-ray-cluster constraints.

Validate in order:

1. one complete Group reaches the first-request barrier;
2. the full model/tool loop completes, with expected text, reasoning, tool calls, call IDs, arguments, finish reasons, usage, and protocol-specific JSON or Buffered SSE terminal shape in the Relax response;
3. request payloads preserve context lineage and stable tools/template arguments;
4. SessionForest commits the intended leaf or branches;
5. export, reward, and custom advantage match the selected contexts; if custom advantage can return None, the whole-Group drop and replenishment path is observed;
6. agent and external resources clean up;
7. a meaningful multi-sample run reaches transfer and an optimizer step;
8. partial/resume or Eval overlap behavior is exercised when configured.
9. large-scale runs reach their target resident fanout and soak duration without saturating ingress, Shard, permit, or cleanup concurrency lanes.

Use the log markers and state evidence listed in runtime-operations.md. Keep algorithm-specific proof in the corresponding algorithm review rather than expanding this skill.

A one-sample success proves plumbing only. Do not claim training success without meaningful Group behavior, reward evidence, and an optimizer step.

Failure routing

Trace failures through this ownership order:

DataSource -> Prepare Group -> first-request barrier -> Runtime lease
-> Session -> model/backend request -> agent process
-> external executor -> Reward -> Transfer

Stay in Agentic diagnosis for context mismatches, external slot exhaustion, first-request barriers, protected Sessions, agent timeouts, exports, rewards, or cleanup. Use debug-hang after evidence points to Ray scheduling, Actor state, placement, resources, or distributed collectives.

References

• context-linearity.md: model-visible history and branch audit
• external-agent-capacity.md: mandatory remote slot calculation
• integration-contract.md: adapter, request, export, and validation contracts
• large-scale-rollout.md: conditional target-scale readiness
• parameter-preflight.md: effective Agentic flags, dependencies, and evidence
• resident-lifecycle.md: resident Group and Session lifecycle
• partial-and-async-lifecycle.md: cross-step park, resume, protection, and close semantics
• agentic-training-contract.md: Agentic export fanout, identity, advantage, Eval, and batching
• runtime-operations.md: process, API, timeout, optional controls, errors, and evidence