Tool Call Sequence Regression Check Prompt

This prompt is for AI platform engineers and observability teams who need to compare tool call sequences from production traces across different prompt versions. The job-to-be-done is detecting regressions in how a model selects, orders, and parameterizes tools after a prompt change—before those regressions corrupt downstream workflows or user experiences. You should use this prompt when you have access to production trace data, a defined set of expected tools and their schemas, and a need to automate regression detection as part of a release gate or continuous testing pipeline.

The ideal user has access to trace storage (e.g., LangSmith, Arize, or an internal logging system), knows the tool schemas and expected call graph for the workflow under test, and can provide before-and-after trace pairs for the same or comparable inputs. Required context includes the tool definitions, the expected sequence or valid call graph, and any known acceptable variations (e.g., optional tools, argument defaults, or parallel calls). This prompt is not a replacement for semantic output evaluation, latency analysis, or user-facing quality checks—it focuses narrowly on tool call structure and argument correctness.

Do not use this prompt when you lack ground-truth tool schemas, when the workflow has no defined tool call expectations, or when the regression you are investigating is purely textual rather than structural. This prompt also should not be used in isolation for high-risk domains such as healthcare, finance, or safety-critical systems—pair it with human review, schema validation harnesses, and trace-level assertion checks. For workflows where tool calls are non-deterministic by design (e.g., exploratory agents), adapt the expected call graph to permit valid variations rather than enforcing a single rigid sequence.

This section provides a copy-ready prompt template for the Tool Call Sequence Regression Check. The template is designed to be dropped into an evaluation harness that supplies production traces, reference traces, and tool schemas. It instructs the model to perform a structured comparison of tool call sequences—checking for incorrect tool selection, argument drift, missing calls, and ordering regressions—and to output a machine-readable diff report. The template uses square-bracket placeholders for all variable inputs, making it safe to template in code without accidental token substitution.

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You are an AI regression analyst comparing tool call sequences from two prompt versions.

Your task is to produce a structured diff report identifying regressions in tool selection, argument values, call ordering, and call presence.

## INPUTS

### Production Trace (New Version)
[PRODUCTION_TRACE]

### Reference Trace (Previous Version)
[REFERENCE_TRACE]

### Tool Schemas
[TOOL_SCHEMAS]

### Expected Call Graph
[EXPECTED_CALL_GRAPH]

### Risk Level
[RISK_LEVEL]

## OUTPUT SCHEMA
Return a JSON object with this exact structure:
{
  "summary": {
    "total_calls_production": <int>,
    "total_calls_reference": <int>,
    "regression_count": <int>,
    "severity": "none" | "low" | "medium" | "high" | "critical",
    "verdict": "pass" | "fail" | "review_required"
  },
  "regressions": [
    {
      "type": "incorrect_tool" | "argument_drift" | "missing_call" | "extra_call" | "ordering_regression",
      "severity": "low" | "medium" | "high" | "critical",
      "production_call_index": <int | null>,
      "reference_call_index": <int | null>,
      "tool_name": "<string>",
      "description": "<string describing the regression>",
      "production_value": "<string | null>",
      "reference_value": "<string | null>",
      "schema_violation": "<string | null>"
    }
  ],
  "call_graph_compliance": {
    "expected_path": "<string>",
    "actual_path": "<string>",
    "deviations": ["<string>"]
  }
}

## CONSTRAINTS
- Compare calls in execution order, not by index alignment alone.
- Flag any tool call that does not match the provided tool schemas.
- If a tool is called with arguments that violate the schema, mark it as argument_drift with the schema_violation field populated.
- If the expected call graph specifies a required sequence, flag any deviation as an ordering_regression.
- For [RISK_LEVEL] = "high" or "critical", require exact argument matching; for "low", allow semantically equivalent arguments.
- If no regressions are found, return an empty regressions array and severity "none".
- Do not hallucinate tool names or schemas not present in [TOOL_SCHEMAS].

To adapt this template, replace each square-bracket placeholder with data from your trace comparison pipeline. [PRODUCTION_TRACE] and [REFERENCE_TRACE] should contain the full serialized tool call sequences, typically as JSON arrays of function call objects. [TOOL_SCHEMAS] should include the JSON Schema definitions for every tool that may appear. [EXPECTED_CALL_GRAPH] defines the valid call sequences—this can be a directed graph, an ordered list, or a state machine description. [RISK_LEVEL] controls comparison strictness: use "low" for informational diffs, "medium" for standard regression gates, and "high" or "critical" for safety-sensitive or financial workflows where any argument drift is unacceptable. After adapting, always validate the output against the schema before accepting the regression verdict. For high-risk domains, route failures to human review rather than auto-blocking a release.

This prompt is designed to be a programmatic gate in a CI/CD or release workflow, not a one-off manual review. It expects two structured inputs: a reference trace (the 'golden' or previous version's tool call sequence) and a candidate trace (the new version's sequence). The harness must serialize these traces into a consistent JSON format before calling the model, ensuring that tool names, argument schemas, and call order are represented uniformly. The model's output is a structured diff report, which the harness must parse and validate against a known schema before any automated decision is made.

To wire this into an application, build a validation layer around the model's output. After receiving the JSON diff report, the harness should: (1) validate the output against a strict JSON schema that defines the expected fields (sequence_diff, regression_flags, severity, root_cause_hypothesis); (2) cross-reference any flagged tool names against the actual tool registry to ensure the model didn't hallucinate a tool; (3) compare the reported argument drift against a schema validator for each tool's parameters. If the output fails validation, implement a retry loop with a maximum of 2 attempts, feeding the validation errors back into the prompt's [CONSTRAINTS] section. Log every validation failure and retry attempt for observability. For high-risk deployments, route any severity: "critical" diff to a human-in-the-loop review queue before blocking the release.

Model choice matters here. This task requires strong instruction-following and structured output capabilities. Prefer GPT-4o, Claude 3.5 Sonnet, or Gemini 1.5 Pro with JSON mode or structured output features enabled. Set temperature to 0 to minimize variance in the diff report. The harness should also implement a semantic equivalence check for argument drift: a simple string diff is insufficient. Use a secondary LLM judge call or a schema-aware comparator to determine if an argument change is a true regression (e.g., file_path changed from /data/reports to /tmp/cache) or a cosmetic difference. Finally, store the full prompt, both traces, the raw output, and the validation result in an audit log tied to the prompt version and release ID. This creates a traceable record for governance reviews and future regression debugging.