Close the Agent Reliability Loop.
AI agents work in demos. Reliable behavior in production is the hard part. AgentMark gives them the same reliability loop your code already has: prompts, datasets and evals in git, testing in CI, traces in your OTel stack.
Your agents are degrading right now.
Most teams find out from users, not dashboards. Here's what that looks like.
Silent regression
−23%
response quality
Prompt shipped Monday. Response quality dropped 23%. No alert fired. A user complained on Friday.
Caught 4 days later
Runaway loop
$47,000
total damage
Two agents got stuck coordinating. Week 1 cost $127. Week 4 cost $18,400. The team mistook the spike for user growth.
Caught on the invoice
Fluent failure
200 OK
HTTP status
Agent called a tool with a wrong parameter. Database returned zero rows. The agent told the user: "I couldn't find any data." Every dashboard stayed green.
Never caught
The fix is the same one that works for code: evals, observability, alerts, and version control.
That's AgentMark.
The reliability loop your agents are missing.
Your code has had this loop for years. Your agents haven't — until now.
git commit
Edit your Agent, dataset, or eval in your repo
Prompts + DatasetsCI runs evals
Regressions block the PR — not your users
Evaluations in CIgit merge
Green evals gate every deploy to main
Git-native deploydiagnose + fix
Diagnose the trace, add the test case & fix
Back to step 01alert fires
Quality, cost, or latency crosses your threshold
AlertsOTel traces
Every span, token, and tool call captured
Tracing + MetricsAgentMark catches regressions before deploy with evals in CI, then catches what slips through with OTel and alerts.
git commit
Edit your Agent, dataset, or eval in your repo
Prompts + DatasetsCI runs evals
Regressions block the PR — not your users
Evaluations in CIgit merge
Green evals gate every deploy to main
Git-native deployOTel traces
Every span, token, and tool call captured
Tracing + Metricsalert fires
Quality, cost, or latency crosses your threshold
Alertsdiagnose + fix
Diagnose the trace, add the test case & fix
Back to step 01AgentMark catches regressions before deploy with evals in CI, then catches what slips through with OTel and alerts.
Catch problems before your users do.
Metrics, traces, prompts, datasets, evals, experiments, and alerts — all connected, all in your repo.
Metrics
Know your cost, latency, and error rate before a user complains — not after.
- Know your cost per request and error rate before a user complains
- Track latency and quality trends across model versions and prompt changes
- Correlate token usage spikes with specific prompt or code changes
“AgentMark is, by far, the best agent representation layer of this new stack. You're the only people I've seen that take actual developer needs seriously in this regard.”
Dominic Vinyard
Founding AI Designer
San Francisco, CA
Works with your entire stack.
No proprietary SDKs. Standard OpenTelemetry for traces, git for version control, and direct support for every major model and framework.
Works in your existing editor.
Prompts, datasets, and evals are just files. Your AI assistant can read, write, and refactor them like any other code.
Prompts live in your codebase.
Your AI editor gets hooked up to AgentMark docs via MCP. Ask Claude Code or Cursor to generate a prompt, update parameters, or refactor your system message — no context switch needed.
Your data, your repo, your standards.
Closed platforms own your data and lock you into their SDKs. AgentMark doesn't.
Questions
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