ai-contract-observer

A lightweight observability layer for AI systems.

This project solves a common problem in AI applications: even when requests and responses are structured, teams still struggle to understand what actually happened during a model interaction. Inputs are hard to trace, failures are difficult to diagnose, and system behavior becomes harder to improve over time.

ai-contract-observer provides a small, practical logging and summary layer for AI interactions. It is designed to work especially well with ai-contract-kit but it can also be used with any structured request and response objects.

It can be used as both a reference implementation and a lightweight standard for observing AI interactions.

Why this project exists

Most AI systems are still hard to inspect.

• One request succeeds but no one knows why.
• Another fails and the error is only visible in application logs.
• A third returns an uncertain output, but the system records it as a generic success.
• A fourth performs poorly over time, but there is no consistent way to measure behavior across runs.

The result is weak traceability, poor debugging, and limited operational visibility.

This package defines a simple observation layer that sits around the model interaction boundary.

Mental model

Think of the package as a small observability boundary:

App -> Request Contract -> Model -> Response Contract -> Observation Layer

The observer sits beside the application flow and records what happened in a consistent format.

This does not replace model SDKs, tracing platforms, or application logging.

It standardizes how AI interactions are logged, summarized, and inspected.

What is included

• Observer instance for recording AI interactions.
• Normalized event entries with IDs, timestamps, and durations.
• Console logger for development visibility.
• JSON logger for machine-readable output.
• Summary helpers for status counts and success rates.
• Example usage demonstrating real-world integration.
• Test coverage for core observer behavior.

Install

npm install ai-contract-observer

Example

import { createObserver } from "ai-contract-observer";

const observer = createObserver();

const request = {
  version: "1.0",
  task: "summarization",
  prompt: "Summarize this text in 3 sentences."
};

const response = {
  version: "1.0",
  status: "success",
  output: {
    text: "This is the summary."
  },
  meta: {
    model: "example-model-v1"
  }
};

observer.observe({
  request,
  response,
  durationMs: 842
});

console.log(observer.getSummary());

Event structure

An observation entry looks like this:

{
  "id": "obs_000001",
  "timestamp": "2026-03-22T00:00:00.000Z",
  "durationMs": 842,
  "request": {
    "version": "1.0",
    "task": "summarization",
    "prompt": "Summarize this text in 3 sentences."
  },
  "response": {
    "version": "1.0",
    "status": "success",
    "output": {
      "text": "This is the summary."
    },
    "meta": {
      "model": "example-model-v1"
    }
  }
}

Status summary

The observer tracks the following response statuses:

• success
• uncertain
• refusal
• error
• unknown

This makes it easier to distinguish between valid results, low-confidence outputs, refusals, system errors, and malformed responses.

Quick wrapper example

import { createObserver } from "ai-contract-observer";

async function runModel(modelClient, request) {
  const observer = createObserver();

  const start = Date.now();

  try {
    const raw = await modelClient.generate(request);

    const response = {
      version: "1.0",
      status: raw?.status ?? "unknown",
      output: {
        text: raw?.text ?? null,
        structured: raw?.structured ?? null
      },
      meta: {
        model: raw?.model ?? "unknown"
      }
    };

    const entry = observer.observe({
      request,
      response,
      durationMs: Date.now() - start
    });

    return { response, entry };
  } catch (error) {
    const response = {
      version: "1.0",
      status: "error",
      output: {
        text: null,
        structured: null
      },
      issues: [
        {
          type: "exception",
          message: error instanceof Error ? error.message : "Unknown error"
        }
      ],
      meta: {
        model: "unknown"
      }
    };

    const entry = observer.observe({
      request,
      response,
      durationMs: Date.now() - start
    });

    return { response, entry };
  }
}

API

createObserver(options?)

Creates a new observer instance.

Supported options:

• logger: an object with a log(entry) function.
• idPrefix: string prefix for generated observation IDs.
• startSequence: starting integer for generated IDs.

observer.observe({ request, response, durationMs, timestamp? })

Records a single AI interaction and returns the normalized observation entry.

observer.getEntries()

Returns a copy of all recorded entries.

observer.getSummary()

Returns aggregate counts and derived metrics across recorded entries.

observer.clear()

Removes all stored entries from the current observer instance.

Design Principles

This project is intentionally minimal.

It defines a small, explicit observability layer rather than a full telemetry platform. The goal is to provide a stable and understandable boundary for recording AI interactions without introducing unnecessary complexity.

The design emphasizes:

• Simplicity over abstraction
• Explicit event structure over implicit logging
• Reliability over feature sprawl
• Composability over completeness

This allows the observer to work across different models, tools, and systems while remaining easy to adopt.

Non-Goals

This project does not attempt to:

• Replace model SDKs or providers.
• Replace full observability platforms.
• Provide dashboards or hosted telemetry infrastructure.
• Enforce a specific prompt strategy or orchestration framework.

It focuses only on defining a consistent observation layer for AI interactions.

Roadmap

This project is designed as a foundation for more observable AI systems. Future extensions may include:

• File persistence adapters.
• Streaming event hooks.
• OpenTelemetry-compatible emitters.
• Dashboard and reporting utilities.
• Evaluation pipeline integration.
• Trace correlation across multi-step AI workflows.

License

MIT