Helicone
AI gateway and LLM observability
Provider routing, fallbacks, request logs, costs, latency, prompts, sessions, rate limits, alerts, and AI app debugging.
// agentpm vs helicone
AI gateway observability is not coding-agent work observability.
Helicone routes, debugs, and analyzes AI applications. 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
Helicone observes AI traffic. AgentPM observes coding-agent engineering work.
Helicone is useful when model requests are flowing through your AI app. AgentPM is useful when coding agents are doing software work on developer machines.
Helicone
AgentPM
Routes and observes LLM traffic across providers and models.
Observes coding-agent work across prompts, commands, files, tests, and decisions.
Tracks request logs, cost, latency, errors, prompts, and application sessions.
Tracks engineering execution before GitHub, Jira, or production has a clean record.
Helps answer "What happened to this model request or AI app flow?"
Helps answer "What did the coding agent actually do in the workspace?"
Optimizes provider choice, routing reliability, cost, and LLM debugging.
Optimizes coding-agent usage, review quality, and team execution patterns.
Uses the LLM request, gateway route, or app session 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
Helicone watches AI applications run.AgentPM watches software get built.
Use Helicone when you need AI gateway and LLM observability.
It is the right layer for routing model traffic, monitoring requests, tracking costs and latency, managing prompts, and debugging AI applications.
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.