Agent Tool Chain Rollback Test Prompt

This prompt is for agent developers and AI quality engineers who need to verify that a multi-step agent correctly handles a mid-chain tool failure. The job-to-be-done is generating a test specification that defines the expected rollback or compensation behavior and produces machine-readable assertions to validate state consistency after a partial failure. You should use this prompt when you have a defined sequence of tool calls with known dependencies, and you need to prove that your agent's error-recovery logic—not just its happy path—works before production deployment.

The ideal user has a concrete tool orchestration plan in hand: a list of tool names, their arguments, the order of execution, and the state each tool is expected to modify. The prompt requires you to specify exactly which step in the chain fails, the failure mode (e.g., timeout, authorization error, malformed response), and the expected compensation actions. Without this context, the prompt cannot generate meaningful assertions. You must also provide the expected final state so the generated test can compare actual versus expected outcomes.

Do not use this prompt for single-step tool calls, where rollback is trivial or unnecessary. It is also inappropriate for workflows where tool calls are fully independent and have no side effects that require compensation. If your agent's error handling is undefined or purely best-effort, this prompt will expose that gap rather than paper over it. For high-risk domains such as financial transactions, healthcare operations, or infrastructure mutators, always require human review of the generated test plan before execution, and ensure the assertions are run in a sandboxed environment against mocked tool responses, never against live production systems.

This prompt template is designed to be dropped into a test harness that simulates a multi-step agent workflow. You supply the sequence of tool calls, the point of failure, and the expected state invariants. The model returns a structured set of assertions that verify whether the agent correctly rolled back or compensated for the partial failure. Use this before deploying any agent that chains writes, mutations, or irreversible side effects.

textCopy
You are an agent reliability engineer testing multi-step tool chain integrity.

Given the following:
- A sequence of tool calls the agent attempted: [TOOL_CALL_SEQUENCE]
- The step index where a failure occurred: [FAILURE_STEP_INDEX]
- The failure details (error type, message, tool response): [FAILURE_DETAILS]
- The expected rollback or compensation behavior: [EXPECTED_BEHAVIOR]
- The state invariants that must hold after recovery: [STATE_INVARIANTS]
- The tool contract schemas for all involved tools: [TOOL_CONTRACTS]

Produce a structured test specification with the following sections:

1. **Failure Summary**: Restate what failed, at which step, and why.
2. **Expected Rollback/Compensation Steps**: List the exact tool calls the agent should make to recover, in order, with expected arguments.
3. **State Assertions**: For each invariant in [STATE_INVARIANTS], generate a concrete assertion that can be programmatically verified. Include the tool or data source to query, the expected value or condition, and the comparison operator.
4. **Idempotency Checks**: If any rollback step could be retried, specify the idempotency key behavior and the expected outcome of duplicate execution.
5. **Degradation Notes**: If full rollback is impossible, describe the acceptable degraded state and any required human notification.
6. **Test Pass/Fail Criteria**: Define the exact conditions that determine whether the agent's recovery behavior passes or fails.

Output the result as a JSON object matching this schema:
[OUTPUT_SCHEMA]

Constraints:
- Do not assume the agent succeeded at any step after [FAILURE_STEP_INDEX].
- If a rollback step itself could fail, include a secondary recovery path.
- Flag any invariant that cannot be verified automatically and requires human review.
- If [EXPECTED_BEHAVIOR] is "none" or "unknown," state that explicitly and generate assertions that detect silent failure.

To adapt this template, replace each square-bracket placeholder with concrete values from your test scenario. The [TOOL_CALL_SEQUENCE] should be a JSON array of step objects, each containing the tool name, arguments, and expected success response. The [OUTPUT_SCHEMA] should be a JSON Schema that your test runner can validate—define required fields, enum values for pass/fail, and a human_review_required boolean. If your agent uses idempotency keys, include the key generation pattern in [TOOL_CONTRACTS] so the model can verify correct reuse. For high-risk domains such as financial transactions or clinical data mutations, always route the generated assertions through human review before trusting them as the sole gate.

The Agent Tool Chain Rollback Test Prompt is designed to be invoked programmatically as part of a test suite, not as a one-off manual check. The prompt expects a structured input containing the sequence of tool calls, the failure point, and the expected compensation behavior. The output is a set of executable assertions that can be parsed and run against your agent's actual state after a simulated failure. This section covers the integration points, validation layers, and operational considerations for making this prompt a reliable gate in your agent development lifecycle.

To wire this into an application, wrap the prompt in a test harness function that accepts a ToolChainScenario object with fields: tool_call_sequence (ordered list of tool invocations with arguments), failure_step_index (zero-based index where the failure is injected), failure_type (e.g., timeout, auth_error, malformed_response, partial_write), and expected_rollback_behavior (natural language description of the compensation logic). The harness should call the LLM with the prompt template, parse the JSON response into a list of Assertion objects (each containing assertion_type, target_state, expected_value, and comparison_operator), and then execute those assertions against the actual system state after replaying the tool chain with the failure injected at the specified step. Validation: Before executing assertions, validate that the LLM output contains only assertions referencing declared tool outputs and state keys—reject any assertion that references undefined state. Retries: If the LLM returns malformed JSON or assertions that fail schema validation, retry once with the validation errors appended to the prompt as [CONSTRAINTS]. Logging: Log the full prompt, response, parsed assertions, and assertion results to your test observability system (e.g., structured JSON logs with trace IDs) so that rollback test failures are debuggable across agent versions.

Model choice: Use a model with strong structured output and reasoning capabilities (e.g., GPT-4o, Claude 3.5 Sonnet) because the task requires inferring implicit state dependencies and compensation logic from tool descriptions. Avoid smaller or faster models that may hallucinate state keys or produce unparseable assertion structures. Tool use: This prompt does not require the model to call tools; it is a pure text-to-JSON generation task. However, the assertions it produces will be consumed by your test framework's tool-calling layer. Human review: For high-risk agent workflows (financial transactions, database migrations, destructive operations), a human should review the generated assertions before they become part of the automated test suite. Store the reviewed assertion set as a version-controlled artifact alongside the tool chain definition. What to avoid: Do not run this prompt against production systems. The failure injection step must occur in a sandboxed or ephemeral environment where partial writes and rollbacks cannot affect real data. If your agent's tools lack idempotency keys or compensation endpoints, the assertions will be aspirational rather than testable—fix the tool contracts first.