Coding-agent work
Prompts, plans, shell commands, edits, tests, retries, and decisions happen inside local development sessions.
// comparisons
AgentPM goes beyond AI observability.
Observability explains how AI systems perform. AgentPM preserves how software work gets done: the requests, commands, code changes, decisions, tests, outcomes, and handoffs that need to carry forward.
The tl/dr
AgentPM is the work-history layer for coding agents. Observability tools show model and app behavior; AgentPM keeps the software-work record they cannot see.
// where each tool sits
AgentPM starts before the clean artifact exists.
Most software records begin after the work is summarized. AgentPM preserves the messy, useful work trail that comes before GitHub, Jira, production telemetry, or eval dashboards can explain what happened.
AgentPM
Software artifact
Commits, branches, PRs, docs, tickets, and release notes become the visible record most teams already review.
AI application runtime
Model requests, traces, evals, latency, cost, feedback, and guardrails appear when AI systems are tested or running.
// where AgentPM fits
The work record observability cannot see.
Langfuse, Braintrust, Helicone, Future AGI, Arize, and Weights & Biases help teams trace, evaluate, guard, and monitor AI systems. AgentPM preserves the development history created while coding agents do the work so humans can review it and future agents can continue it.
Platform categoryBest forPrimary record
AgentPMCoding-agent work history and continuityConversations, commands, code changes, decisions, tests, outcomes, open work, and handoffs.
Observability and evaluation platforms
Langfuse, Braintrust, HeliconeAI application observability and evaluationTraces, prompts, evaluations, latency, cost, routing, and application behavior.
Future AGIAgent evaluation and guardrailsFailure detection, evaluation datasets, regressions, safety checks, and runtime protections.
Arize AI, Weights & BiasesBroader AI development, observability, and evaluationAgent and application traces, evaluations, monitoring, model experiments, and production performance.
// agentpm evidence
What disappears without a work-history layer.
Coding agents leave behind more than a diff. AgentPM keeps the development signals teams need for review, handoff, coaching, governance, and repeated improvement.
Plans and intent
What the agent thought it was doing before it touched code.
Commands and output
The real shell work, failures, retries, and verification trail.
Files and changes
Which files moved, why they moved, and how the implementation evolved.
Tests and proof
What was run, what passed, what failed, and what remains unproven.
Branches and handoff
How local work travels toward commits, pull requests, and deployment.
Decisions and risks
Tradeoffs, reversals, open questions, and follow-up context worth keeping.
// comparison pages