Agent Trace Analysis

Analyse an AI agent's run trace for potential failures and token waste. Returns findings, likely causes and suggested fixes.

Pay by wallet (x402) AI-generated agent-trace-analyze

Example

Input

{
  "trace": "test_value"
}

Answer

{
  "severity": "warning",
  "root_cause": "The trace input 'test_value' is not a valid AI agent execution log. It lacks essential components including: timestamped steps, tool invocations, agent reasoning, state transitions, or error messages. Cannot analyze execution flow without structured trace data.",
  "step_count": 0,
  "failure_type": "none",
  "trace_length": 10,
  "failure_point": "Trace input validation - insufficient execution data provided",
  "steps_summary": [],
  "suggested_fix": "Provide a complete agent execution trace with: (1) timestamped sequential steps, (2) agent thoughts/reasoning, (3) tool names and parameters, (4) tool outputs/responses, (5) any error messages or exceptions, (6) final state or outcome",
  "patterns_detected": [
    "insufficient_trace_data",
    "missing_execution_steps",
    "cannot_analyze_without_structured_log"
  ],
  "token_usage_breakdown": {
    "total": 97,
    "estimated_input_tokens": 12,
    "estimated_output_tokens": 85
  }
}

Inputs

Field	Type	Required	What it is
`trace`	string	Yes	Agent log/trace output

Output fields

How reliably each field appears in a successful answer.

Field	Type	Appears
`root_cause`	string	Always (present in every successful answer)
`step_count`	integer	Always (present in every successful answer)
`failure_type`	string	Always (present in every successful answer)
`failure_point`	string	Always (present in every successful answer)
`suggested_fix`	string	Always (present in every successful answer)

How to call it

The same tool through each route. MCP clients reach it through Strale’s server at https://api.strale.io/mcp; the call below is what the agent sends.

# Needs an API key. max_price_cents caps what this call may cost.
curl -X POST https://api.strale.io/v1/do \
  -H "Authorization: Bearer $STRALE_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "capability_slug": "agent-trace-analyze",
    "inputs": {
      "trace": "test_value"
    },
    "max_price_cents": 20
  }'

{
  "tool": "strale_execute",
  "arguments": {
    "slug": "agent-trace-analyze",
    "inputs": {
      "trace": "test_value"
    },
    "max_price_cents": 20
  }
}

import { Strale } from "straleio";

const strale = new Strale({ apiKey: process.env.STRALE_API_KEY! });
const result = await strale.do({
  capability_slug: "agent-trace-analyze",
  inputs: {
    "trace": "test_value"
  },
  max_price_cents: 20,
});

import os
from straleio import Strale, DoRequest

strale = Strale(api_key=os.environ["STRALE_API_KEY"])
result = strale.do(DoRequest(
    capability_slug="agent-trace-analyze",
    inputs={
        "trace": "test_value",
    },
    max_price_cents=20,
))

# No account. The first request answers 402 with the price in USDC;
# an x402 client pays it from your wallet and repeats the request.
curl -i 'https://api.strale.io/x402/v2/agent-trace-analyze?trace=test_value'

Limitations

Complex trace bottlenecks can be missed
AI analysis of agent traces — may not identify all performance bottlenecks in complex multi-agent orchestrations
What to do instead: Provide the full trace including timestamps and tool outputs for the most actionable analysis