Building Dashboards for AI Agents: Observability for Autonomous Code Workers

AI coding agents are moving fast — from novelty demos to actual workers running in your CI pipelines, reviewing pull requests, and shipping code while you sleep. That shift is exciting, but it creates an uncomfortable gap: how do you know what an agent actually did? Agents are non-deterministic. The same prompt can produce different tool call sequences, and multi-step workflows can drift in subtle ways that only become obvious after the damage is done. The answer is the same one we use for distributed systems: structured logs and a dashboard you can actually read.

Why Agents Need Dashboards

Traditional monitoring was designed for processes that behave predictably. An agent is not that. It decides which tools to call, in which order, based on context it inferred from somewhere. A few things follow from this:

Non-determinism: You cannot replay a run by re-reading the code. You need a trace of what the agent actually did.

Multi-step workflows: A deployment agent might touch a dozen systems in sequence. If it fails at step 8, you want to know exactly where and why — not just that the pipeline is red.

Cost tracking: Agents that chain LLM calls accumulate token usage fast. Without visibility, a single runaway agent can quietly rack up significant costs.

Multi-agent coordination: When multiple agents work in parallel — say, a code reviewer and a deployment agent — each needs to be able to check the state the other is leaving behind.

A LogBlazer project gives you a home for all of this. One API endpoint, structured log levels, named sources, and monitor widgets that expose real-time state to anything that can make an HTTP request — including other agents.

Four Agent Dashboard Setups Worth Building

Code Review Agent

A code review agent reads a pull request, checks each file, and posts inline suggestions. The dashboard for this agent should answer: what did it review, what did it flag, and is its verdict trustworthy right now?

Monitors: review_status (text — idle / running / done), files_reviewed (number), issues_found (number), test_pass (boolean).

Log sources: reviewer for per-file decisions, test-runner for test outcomes, linter for static analysis results.

Each file the agent reviews, it logs a line:

curl -s -X POST "https://logblazer.com/api/logs/YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "message": "Reviewed src/auth/login.ts -- 2 suggestions, 0 critical issues", "level": "info", "source": "reviewer", "monitors": [ { "key": "files_reviewed", "value": 14 }, { "key": "issues_found", "value": 6 } ] }'

Deployment Agent

Deployment agents orchestrate builds, run tests, push artifacts, and promote across environments. The failure modes are real and often noisy. A clean dashboard cuts through that.

Monitors: pipeline_status (text — building / testing / deploying / done / failed), environment (text — staging or production), deploy_ok (boolean), rollback_triggered (boolean).

Log sources: ci-runner, test-suite, deployer, health-check.

When the agent promotes to production, it logs:

curl -s -X POST "https://logblazer.com/api/logs/YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "message": "Deployed v2.4.1 to production -- health check passed", "level": "info", "source": "deployer", "monitors": [ { "key": "pipeline_status", "value": "done" }, { "key": "deploy_ok", "value": true }, { "key": "environment", "value": "production" } ] }'

Data Pipeline Agent

ETL agents ingest, transform, and load data on a schedule. They fail quietly — a bad transform produces no error, just wrong data. Monitors make the silent failures visible.

Monitors: etl_status (text), records_processed (number), error_rate (number — percentage), last_run_ok (boolean).

Log sources: ingester, transformer, loader, validator.

After each batch, the agent updates state and logs the outcome:

curl -s -X POST "https://logblazer.com/api/logs/YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "message": "Batch 42 complete -- 18,430 records loaded, 3 validation errors skipped", "level": "warning", "source": "loader", "monitors": [ { "key": "etl_status", "value": "idle" }, { "key": "records_processed", "value": 18430 }, { "key": "error_rate", "value": 0.016 }, { "key": "last_run_ok", "value": true } ] }'

Support Agent

Support agents handle incoming tickets, draft responses, and escalate when needed. The key metrics are queue depth and response time — if those drift, the agent is struggling.

Monitors: tickets_open (number), avg_response_time_s (number), escalation_rate (number), agent_status (text — active / paused).

Log sources: ticket-handler, responder, escalator.

Each customer interaction gets a log entry:

curl -s -X POST "https://logblazer.com/api/logs/YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "message": "Ticket #4821 resolved -- billing question, auto-answered, confidence 0.94", "level": "info", "source": "ticket-handler", "monitors": [ { "key": "tickets_open", "value": 12 }, { "key": "avg_response_time_s", "value": 8 } ] }'

The Feedback Loop: Agents Reading Their Own Dashboards

This is where it gets genuinely useful. LogBlazer exposes a plain-text, LLM-friendly dashboard endpoint:

GET https://logblazer.com/d/YOUR_PROJECT_ID/llm

The response is structured markdown — monitor states, recent log lines, source summaries. An agent can fetch this at the start of a run to orient itself before taking action:

#!/bin/bash # Agent reads current state before deciding what to do next DASHBOARD=$(curl -s "https://logblazer.com/d/YOUR_PROJECT_ID/llm")

# Inject dashboard context into the agent prompt PROMPT="Here is the current system state:

$DASHBOARD

Based on this, decide whether to proceed with deployment."

# Agent makes an informed decision instead of acting blind curl -s -X POST "https://api.example.com/agent/run" \ -H "Content-Type: application/json" \ -d "{\"prompt\": \"$PROMPT\"}"

This creates a tight, practical feedback loop. Before an agent acts, it reads the dashboard. After it acts, it writes to the dashboard. Another agent — or a human — can read that same dashboard and know exactly where things stand. The public dashboard URL means you can also link it in a pull request description, a Slack message, or a status page, so the whole team has visibility without any extra tooling.

In multi-agent setups, this pattern is especially clean. A code review agent writes its verdict to a shared project. The deployment agent reads that project's LLM endpoint before deciding whether to proceed. No custom API, no shared database — just one endpoint each agent already knows how to use.

Getting Started

Create a project on LogBlazer, grab your API key, and have your agent make its first log call. The setup takes under a minute. From there, add monitors as you discover what state you actually care about — you do not need to design the perfect dashboard upfront. Let the agent run a few times, see what questions you end up asking, and add monitors for those. That is the honest way to build observability: start minimal, add what earns its place.