Braintrust
AI product evals and observability
Evaluations, prompt iteration, datasets, scorers, traces, experiments, and production quality workflows for AI products.
// agentpm vs braintrust
AI product quality and coding-agent execution need different records.
Braintrust helps measure AI product behavior. AgentPM watches software get built by coding agents.
different problems / complementary layers
AgentPM
Coding-agent SDLC observability
Plans, commands, file edits, tests, retries, decisions, and the evidence trail behind software work.
// key differences
Braintrust evaluates AI outputs. AgentPM evaluates the work trail behind software creation.
Braintrust is oriented around AI product quality. AgentPM is oriented around coding-agent engineering execution.
Braintrust
AgentPM
Tracks AI product behavior, experiments, prompts, datasets, scorers, and eval results.
Tracks coding-agent plans, commands, edits, tests, retries, unresolved work, and decisions.
Helps improve AI application quality through evals and observability.
Helps improve engineering efficiency through coding-agent work evidence.
Helps answer "Did this AI system produce the right result?"
Helps answer "How did this coding agent attempt the engineering task?"
Focuses on prompt, model, dataset, and scorer iteration.
Focuses on session review, workflow coaching, and software-work governance.
Uses the evaluation, trace, experiment, or prompt version as the unit of analysis.
Uses the software work session as the unit of analysis.
// what agentpm captures
The session is the artifact.
AgentPM captures the work trail that usually disappears between a developer's local coding-agent session and the final PR.
Plans
What the coding agent intended to do before editing.
Commands
The shell work, outputs, failures, and retries behind the result.
File Edits
Which files changed and how the implementation moved.
Tests
What was verified, skipped, retried, or left unproven.
Branches
The route from local session work toward PRs and commits.
Decisions
The why behind tradeoffs, reversals, and handoff context.
// compounded knowledge
The optimization loop is different for coding agents.
Observability is not just about catching failures. The long-term value is turning repeated behavior into better future performance.
For user-facing agents, knowledge compounds into better experiences.
Customer context, prior interactions, feedback, and evaluation data help AI products become more useful, personalized, and reliable for end users.
For coding agents, knowledge compounds into engineering efficiency.
Plans, commands, file edits, failed tests, reviewer feedback, and decisions become reusable workflow knowledge that reduces retries and improves future coding-agent sessions.
// summary breakdown
Braintrust watches AI applications run.AgentPM watches software get built.
Use Braintrust when you need AI product evals and observability.
It is the right layer for evaluations, prompt iteration, scorer workflows, experiments, and monitoring AI application quality.
Use AgentPM when you need agent-work observability.
It is the right layer for reconstructing how coding agents planned, edited, tested, retried, and decided while building software.
If your question is about model behavior in a deployed AI product, start with AI observability. If your question is about what coding agents did during engineering work, start with AgentPM.
// sources
Comparison source links
These pages summarize public positioning from the adjacent tool and compare it with AgentPM's coding-agent work layer.