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Raju Dandigam
Raju Dandigam

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I built agent-inspect to debug TypeScript AI agent trajectories

#testing #ai #agents #typescript

Your AI agent failed.

Again.

The final answer is wrong, but the logs look fine:

tool call started
model call started
tool call completed
model call completed
fallback used
error: timeout
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Which tool caused the timeout? Did the model answer before retrieval finished? Was the fallback expected? Did the agent call the same tool twice?

This is where console.log stops feeling like debugging and starts feeling like archaeology.

I kept hitting this problem while building TypeScript AI agents. Once the flow moved beyond a single model call, the debugging loop became a system:

plan → retrieve → rank → generate → validate → maybe retry → maybe hand off
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Flat logs lost the structure. Output only tests could miss a bad path that happened to produce a plausible answer. Model graded evals helped with semantic quality, but they were a poor fit for every deterministic CI rule. And raw traces were too risky to paste casually into issues or pull requests.

So I built agent-inspect.

AgentInspect is a local evidence debugger and trajectory-test toolkit for TypeScript AI agents.

It turns one local trace into three things: a readable execution tree, a deterministic regression gate, and a derived Evidence v2 bundle that you can review before sharing.

No account. No collector. No default upload. Metadata only by default.

one local JSONL trace
├─ Debug   → view · report · explain
├─ Prevent → check · contract · CI
└─ Share   → redact · bundle · verify
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The bug is often in the trajectory

A support agent can return a plausible answer after doing almost everything wrong.

The healthy path might be:

plan-request
└─ retrieve_policy
   └─ rank-results
      └─ generate_answer
         └─ policyShown: passed
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The regression might be:

generate_answer          <- answered before retrieval
retrieve_policy
retrieve_policy          <- duplicate call
search_docs              <- wrong tool, failed
policyShown: failed
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An output only test may pass. A flat log may contain every event. Neither makes the wrong path obvious.

The final answer is only one fact about the run. Tool choice, ordering, repetition, completion, duration, token usage, and observed outcomes are facts too. Together, those facts form the agent's trajectory.

That trajectory should be inspectable. It should also be testable.

Make execution boundaries explicit

You can start with manual instrumentation:

import { inspectRun, observeOutcome, step } from "agent-inspect";

const answer = await inspectRun(
  "support-agent",
  async () => {
    const policy = await step(
      "retrieve_policy",
      () => retrievePolicy(),
      {
        type: "tool",
        metadata: { toolName: "retrieve_policy" },
      },
    );

    const result = await step(
      "generate_answer",
      () => draftAnswer(policy),
      {
        type: "llm",
        metadata: { model: "your-model" },
      },
    );

    await observeOutcome("policyShown", {
      expectation: "The answer cites a retrieved policy",
      status: "passed",
      method: "custom",
    });

    return result;
  },
  { traceDir: ".agent-inspect" },
);
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The wrapper records those boundaries as local JSONL while preserving the application's return value and errors. Raw prompts and model outputs are not required for the core workflow.

If your application already emits structured logs or uses AI SDK, OpenAI Agents JS, LangChain, or LangGraph you can use an adapter or reader instead of wrapping every step manually.

Get a useful result without an API key

The shortest path uses a generated synthetic demo:

npm install agent-inspect
npx agent-inspect init --yes
node examples/agent-inspect-demo.mjs
npx agent-inspect list --dir .agent-inspect
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init writes a small config and demo into your project. The demo does not call a model or upload a trace.

Copy the run ID printed by list, then use the same local artifact for the three jobs below.

1. Debug: read the execution tree 

npx agent-inspect view <run-id> --dir .agent-inspect --summary
npx agent-inspect report <run-id> --dir .agent-inspect
npx agent-inspect explain <run-id> --dir .agent-inspect
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A terminal lists a local AgentInspect run and renders its execution tree with step types, durations, model metadata, and token counts

The tree restores the structure that flat logs lose: nested steps, tool and model calls, durations, safe metadata, errors, and observed outcomes. explain summarizes local trace facts deterministically; its default path makes no provider call.

The useful question changes from:

Did the run fail?
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to:

Where did the passing and failing trajectories first diverge?
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That distinction matters when the visible answer looks fine but the agent skipped a required retrieval, safety, or validation step.

2. Prevent: turn the path into a CI gate

Some agent quality questions are subjective. Helpfulness, tone, and open ended answer quality can benefit from model graded evaluation.

But many regressions are structural:

  • Was retrieve_policy called?
  • Did the forbidden search_docs tool appear?
  • Did generation happen before retrieval?
  • Did the run complete?
  • Did an observed outcome fail?
  • Did the agent exceed a duration or token budget?

Those checks do not need another model. They can be deterministic:

npx agent-inspect check <run-id> --dir .agent-inspect \
  --preset trajectory \
  --required-tool retrieve_policy \
  --forbidden-tool search_docs \
  --fail-on-observation failed
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The preset and explicit shorthand rules are additive. A healthy run exits 0; a trajectory-rule failure exits 1.

For the committed regression fixture, the result is concrete:

Check status: fail
Summary: 2 failed, 0 warning(s), 0 error(s)

- outcome.status: Observed outcome count 1 matched [failed].
- tool.usage: Forbidden tool search_docs appeared.
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The same deterministic trajectory check exits zero for the good run and one for the regression

Same trace. Same rules. Same verdict. No model judge and no provider call in the check path.

That makes it suitable for a normal CI step. If your test fixture writes a trace to a stable path:

- name: Run deterministic agent fixture
  run: node run-agent-fixture.mjs

- name: Check agent trajectory
  run: |
    npx agent-inspect check .agent-inspect/ci-run.jsonl \
      --preset trajectory \
      --required-tool retrieve_policy \
      --fail-on-observation failed \
      --evidence-on fail
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--evidence-on fail writes local Evidence for triage when the check fails. It does not upload the artifact.

When CLI flags outgrow one command, the Beta TraceContract API expresses the same expectations in TypeScript:

import { openTraceFile } from "agent-inspect/readers";
import {
  defineTraceContract,
  evaluateTraceContractRead,
} from "agent-inspect/checks";

const read = await openTraceFile("./.agent-inspect/demo-regression.jsonl");

const contract = defineTraceContract({
  run: { requireCompleted: true },
  tools: {
    required: ["retrieve_policy"],
    forbidden: ["search_docs"],
  },
  observations: { failOn: ["failed"] },
});

const result = evaluateTraceContractRead(read, contract);
if (result.status !== "pass") process.exitCode = 1;
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The principle is simple: use deterministic trace facts for structural CI rules, and reserve model grading for semantic quality.

3. Share: derive reviewable Evidence v2

A failing trace is often the best debugging artifact. It can also contain prompts, tool arguments, retrieved documents, customer identifiers, error messages, or secrets.

The collaboration strategy should not be “paste the raw trace into Slack.”

AgentInspect keeps the source trace read-only and creates a derived bundle:

npx agent-inspect verify-safe <run-id> --dir .agent-inspect
npx agent-inspect bundle <run-id> --dir .agent-inspect \
  --profile share \
  --out ./evidence
npx agent-inspect bundle verify ./evidence
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A terminal creates a share-checked Evidence v2 bundle and verifies its listed file hashes offline

The bundle can include:

evidence.html          self-contained offline review surface
evidence.json          versioned manifest and SHA-256 file hashes
trace.jsonl            redacted derived trace
check-results.json     deterministic findings
redaction-report.json  detector summary without secret values
summary.md             human-readable overview
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bundle verify checks the manifest, listed files, hashes, assessment, and provenance offline.

This is an integrity check. It is not a signature or a compliance certificate.

The wording matters: the artifact is share-checked, not “certified safe.” verify-safe and redaction are best-effort controls. Review the generated HTML and safety results before attaching a bundle to a pull request, incident, or public issue.

Use the capture path that matches your stack

The local evidence model is not tied to one agent framework.

Your stack Capture path
Custom TypeScript functions or classes inspectRun, step, observe, or createInspector
Vercel AI SDK @agent-inspect/ai-sdk
OpenAI Agents JS @agent-inspect/openai-agents
LangChain or LangGraph @agent-inspect/langchain
Existing structured logs agent-inspect logs readers
OpenInference or OTLP JSON local standards readers
Vitest or Jest reporters plus experimental trace matchers

The root package is enough for custom capture, the CLI, deterministic checks, and Evidence. Optional packages add only the integration you need.

Optional: give a coding assistant the same facts

The Preview MCP path exposes configured local evidence through bounded, read-only tools:

npx agent-inspect mcp configure --client cursor
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The command is a dry run by default, so you can review the generated configuration before enabling it.

A connected coding assistant can then investigate the same TraceFacts used by the CLI: What failed first? Which required tool was missing? What changed between the passing and failing runs?

This is not replay, an auto fix engine, or a hidden upload path. It is optional read-only access to explicitly configured local evidence.

Where it fits—and where it does not

AgentInspect owns the laptop to pull request evidence loop:

capture locally
→ understand the path
→ fail CI on structural drift
→ derive reviewable evidence
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It complements hosted observability and evaluation platforms. Use hosted tools when you need production dashboards, long term retention, fleet wide alerting, team wide trace search, hosted datasets, or prompt management.

Use AgentInspect when you need to inspect one TypeScript agent run immediately, enforce deterministic trajectory expectations in CI, compare a passing and failing local run, or hand off a redacted, hash-verifiable artifact.

The boundary is intentional. AgentInspect is not:

  • A maintainer hosted SaaS or production APM replacement
  • A hosted trace retention or prompt-management service
  • An LLM as judge or dataset platform by default
  • A replay or automatic remediation engine
  • A chain of thought recorder
  • A compliance certification tool

Try the complete loop

The current release is 6.17.2, requires Node.js 20 or newer, uses persisted schema 1.0, and is MIT licensed. Legacy v0.1 and v0.2 traces remain readable.

npm install agent-inspect
npx agent-inspect init --yes
node examples/agent-inspect-demo.mjs
npx agent-inspect list --dir .agent-inspect
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Then inspect, check, and derive Evidence from the run:

npx agent-inspect view <run-id> --dir .agent-inspect --summary
npx agent-inspect check <run-id> --dir .agent-inspect --preset trajectory
npx agent-inspect bundle <run-id> --dir .agent-inspect \
  --profile share \
  --out ./evidence
npx agent-inspect bundle verify ./evidence
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One local trace should be able to tell you what the agent did, prove that the regression stays fixed, and give a teammate evidence they can review without making upload the price of admission.

How you can help

AgentInspect is open source and MIT licensed. If this workflow is useful to you:

  • Star the repository so more TypeScript agent developers can find it.
  • Try the keyless demo and open an issue if anything feels confusing.
  • Add a starter or recipe for your stack.
  • Share a real debugging workflow you want the project to support.
  • Pick up a good first issue or contribute documentation.

Most of all, leave a comment below.
How do you debug agent runs today?
Which trajectory rule would you put in CI first?
And if AgentInspect does not fit your workflow, tell me why that feedback is just as useful.

Top comments (1)

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reidmarlow profile image
Reid Marlow •

This is the level where agent debugging starts to feel sane. A flat log tells you calls happened. A trajectory tree tells you which branch lied to you. I like the Evidence bundle angle too, since CI needs artifacts a reviewer can read without replaying the whole run.