This update enables AI coding agents to debug Cloudflare Workers autonomously using structured observability data. By providing machine-readable traces and state access locally, it shortens the development loop and reduces the need for manual logging or remote deployments to find errors.
Starting today, wrangler dev and vite dev automatically capture OpenTelemetry traces for local Worker invocations. When Cloudflare's tooling detects an agent session, it points the agent to the Local Explorer API, a local debugging API where it can query those traces. You do not need to install an SDK, enable tracing, configure your agent, or even mention observability in the prompt.
A prompt can be as simple as:
This builds on years of investment in local development, from introducing Miniflare to making local mode the default in Wrangler 3. Local traces give coding agents structured feedback from that development environment before code is deployed.
As part of its normal workflow, an agent starts wrangler dev or vite dev to run and test the Worker. When the development server recognizes a supported coding-agent session, it automatically prints a hint that looks like this:
The Local Explorer is a browser-based interface and REST API for viewing and editing local resource data and querying observability data during development. The API root serves an OpenAPI schema, so agents can discover available endpoints at runtime without hardcoded instructions.
The automatically captured traces are available through a read-only observability endpoint in that API, together with their correlated console logs. The agent can query this telemetry, then use the API's other operations to inspect local Workers and bindings or examine state in D1, KV, R2, Durable Objects, and Workflows.
Consider POST /api/orders, which retrieves an active cart from KV, saves the checkout details into D1, and sends a message to a Queue for order processing. After a schema change, the endpoint suddenly starts returning a 500 status.
The 500 does not identify which operation failed. The agent adds logs around KV, D1, and the Queue, reruns the request, inspects the output, and repeats. Each cycle takes time and burns tokens while the agent reconstructs the request from text.
The agent reproduces the error and queries the read-only observability endpoint. The trace shows that the KV read succeeded, the D1 insert failed with no such column: delivery_window, and the Queue was never called. Your agent uses the Local Explorer API to access the same trace data you would see here:
The agent uses the API to inspect the D1 schema. It finds that the migration adding delivery_window exists in the repository but has not been applied locally, applies it, sends the request again, and queries the new trace. Issue resolved.
In one local loop, the agent identifies the failed operation, fixes the local environment, and verifies the result without deploying or adding temporary logs.
Agents query local telemetry through the API, but you as a human can visualize the same data in the Local Explorer, the browser-based interface built into the local development server. Alongside browsing local binding state, you can select a request to inspect its spans, timing, attributes, errors, and correlated console logs.
Local Explorer runs on the same localhost origin as your Worker, not in the Cloudflare dashboard. Press e in Wrangler or visit /cdn-cgi/explorer on the local server to open it.
When we launched Workers Tracing, we built instrumentation directly into workerd, the open-source runtime that powers Workers. Without requiring an SDK or any code changes, the runtime captures spans for:
fetch to scheduled to queue handlers.Any custom spans emitted by your application will also appear alongside these automatic spans.
Wrangler and the Cloudflare Vite plugin use Miniflare to run your Worker locally in the same runtime, making this instrumentation available during local development.
Miniflare collects runtime events and console output, assembles them into OpenTelemetry traces and correlated logs, then writes the telemetry to an internal SQLite-backed Durable Object that serves as the local trace store. The Local Explorer API exposes that data through the local development server where agents can easily query traces and logs and inspect local state.
Update Wrangler or the Cloudflare Vite plugin, whichever your project uses:
Then ask your agent to debug locally as you normally would. Your agent can already write and run your Worker locally — now it can see what happened, fix what failed, and verify the result before you deploy. Check out the docs to learn more!
Continue reading on the original blog to support the author
Read full articleMoving MCP to a stateless model drastically reduces the complexity and cost of deploying AI agents. Engineers can now build and scale agentic tools using standard serverless infrastructure without managing persistent connections or complex session routing.
Engineers can now debug complex AI agent behaviors beyond simple HTTP status codes. By combining infrastructure-level traces with agent-specific logic and token usage, Cloudflare simplifies the optimization of autonomous systems and reduces the 'black box' nature of LLM workflows.
Traditional auth flows like MFA are bottlenecks for AI agents. By providing temporary, claimable accounts, Cloudflare enables autonomous agentic workflows, allowing AI to code, deploy, and verify software without manual intervention or credential management.
Traditional feature flags add latency or fail in serverless environments. Flagship integrates flags into the edge runtime, enabling safe, high-performance deployments and autonomous AI releases without manual intervention or performance penalties.