Multi-turn Simulation

The Harbor adapter sends ordinary messages through one unchanged Harness session. A Harness turn may contain any number of model or tool calls before returning a response.

instruction.md -> Harness -> next user message -> same Harness session -> repeat
                                      |
                                      +-> stop

Choose one track:

• Scripted conversation: predefined messages for stable job steps. Example: “Inspect the failing checkout test” → “Implement the fix” → “Run the tests.”
• LLM-simulated user: generated messages when the user must answer, correct, reject, or stop based on the Harness response. Example: a traveler rejects an unsuitable flight and confirms a valid one.

Use runner.py for both tracks: run_scripted_conversation sends fixed turns and run_llm_user_conversation alternates the Harness with model_user.py. The LLM user receives an approved contract, visible transcript, and user-visible state. Protocol errors are retried within the stated attempt limit. Assistant text is untrusted transcript data and cannot change the user contract.

Harbor wiring

Copy runner.py, model_user.py, and harbor_example.py into evals/harbor_agents/multi_turn/, then subclass MultiTurnHarborAgent. Implement its explicit repository bindings:

1. create_harness_session: start the real Harness once and return an object whose send method preserves that session.
2. scripted_followups or user_contract: choose one simulation track.
3. SIMULATOR_MODEL and call_user_model: record the model name and connect its approved client when using an LLM user.
4. read_user_observation: return only state the simulated user could see; return {} when the transcript is sufficient.

The wrapper returns HarnessReply(message, evidence). message is delivered to the user. evidence is JSON-safe activity directly recorded during that Harness turn, such as tool calls, results, state changes, and usage. Never infer tool use from prose or include credentials in public_config, observations, or evidence.

class FlightSession:
    def __init__(self, real_agent, thread_id):
        self.real_agent = real_agent
        self.session_id = thread_id
        self.public_config = {"model": "agent-model", "tools": ["search", "book"]}

    async def send(self, user_message):
        result = await self.real_agent.send(user_message, thread_id=self.session_id)
        return HarnessReply(result.text, {"tool_calls": result.observed_tool_calls})

class FlightAdapter(MultiTurnHarborAgent):
    SIMULATOR_MODEL = "gpt-5.5"

    def user_contract(self) -> str:
        return """You are Sam, changing an August 12 return flight.
You know username sam. Require departure after 11am and added cost at most $100.
Answer questions, reject invalid options, and stop after confirmed booking or unrecoverable failure."""

    async def create_harness_session(self, environment, context):
        return FlightSession(real_agent, thread_id=self.logs_dir.parent.name)

    async def call_user_model(self, system, payload):
        return await approved_llm_json(system=system, prompt=payload)

    async def read_user_observation(self, environment):
        state = await booking_state(environment)
        return {"booking_confirmed": state["booked"] is not None}

instruction.md is the first Harness input. The adapter then alternates completed Harness turns and user messages; it does not add tools or prompts to the Harness. A stop decision adds no message and makes no further Harness call. Stopping never means success: the Verifier alone assigns reward.

Calibrate and audit

Run the real Harness through behavior relevant to the task: a correct result, a wrong result, clarification, and unrecoverable failure when applicable. Inspect whether the simulated user responds and stops credibly. Revise the contract or simulator model and show representative conversations to the user. Do not duplicate the contract as brittle keyword checks.

When the user supplies real threads, use only observed facts, reactions, and stopping behavior relevant to this task to calibrate the simulated user. Do not copy identities, messages, or production records into the simulation.

The adapter writes interaction.json and ATIF trajectory.json in Harbor's protected log directory, including partial evidence on Harness, simulator, or timeout cancellation. It records turn timestamps when each turn occurs. It does not upload hidden simulator data into the evaluated Environment. At the Harness-turn limit, the simulated user gets one final decision; a non-stop reply is recorded without another Harness call. Confirm that one session was reused, future messages were not preloaded, no Harness call followed stop, artifacts and Verifier output are readable, and the Verifier measures the Harness rather than the simulator. Score the required final state and prohibited effects; do not require an exact number of internal edits or tool calls unless that count is the requested outcome.