Agentry is for teams shipping software with AI coding agents. It fits best when the agent has repo access, the product has real users, and the team wants answers from errors and analytics events.

Does Agentry require repo access?

Yes. Agentry is most useful when your agent can inspect the repo, wire telemetry, and open reports or fixes from the codebase.

Do I need to be an engineer to use Agentry?

Not by title, but the workflow is technical. Founders, PMs, and operators can ask questions, but the agent needs code and API access.

What data does Agentry use?

Agentry uses errors and analytics events that your app sends over HTTP.

Does Agentry replace Sentry or PostHog?

It can sit beside them or replace the parts you do not need. Agentry's main job is giving your agent one context layer for errors and product events.

What does Agentry cost?

Free includes 50k events per month with 90-day retention. Pro is $39/month for 1M events with 180-day retention. Scale is $149/month for 10M events with 365-day retention.

your app

events

Agentry

questions

answers

Agent-first product analytics and error monitoring.

Agentry gives your agent one API for product analytics and error monitoring, so it can debug failures and explain user behavior from real production data.

Install https://agentry.sh/install.md for me

+ Full access

5.5 Extra High

1. Open your repo in Codex, Claude Code, Cursor etc.
2. Paste the install prompt.
3. Your agent reads the install doc and shows you an implementation plan for approval.

Works with any AI coding agent

How it works

From repo to production data.

Your agent inspects the code, wires errors, events, and deploys, then answers from real data.

Inspect the repo

Finds routes, jobs, webhooks, funnels, failure surfaces, and deploy hooks.

Wire the signals

Adds one HTTP API for errors, analytics events, and deploy attribution.

Get answers

Uses cases, events, deploys, and code context to answer bugs, activation, churn, marketing, or product questions.

Questions you can ask

Ask from production data.

Errors, events, deploys, and repo context give your agent enough evidence to answer and act.

Fix all new cases every 4 hours

codex — ~/myapp

> Every 4 hours, check new Agentry cases, debug each actionable case, and open PRs.▎

I'll check new cases, skip noise, patch actionable bugs, run tests, and open PRs with the case evidence attached.

⏺ Review new cases

└ 5 new · 3 actionable · 2 noise

⏺ Trace source

└ deploys and stack frames point to 3 files

⏺ Patch + test

└ 3 fixes pass locally

⏺ Open PRs

└ one PR per case with Agentry evidence

✓ Wrote src/lib/cases/fixes

→ github.com/acme/app/pulls?q=agentry-case

github.com/acme/app/pulls?q=agentry-case

PRs ready

new production cases patched from Agentry evidence

The scheduled agent triaged every new case and opened fixes for the actionable ones

prs

3

api_auth_failed 184 users

webhook_retry_loop 71 users

export_job_timeout 23 users

automation note one branch per case

Each PR links its Agentry case, affected users, deploy window, source diff, and test output.

Which source creates paid customers?

claude — ~/myapp

> Which signup source creates activated paid accounts, not just signups?▎

I'll rank signup sources by downstream activation and paid conversion, then patch any attribution gap that makes the answer shaky.

⏺ Check readiness

└ source and paid events are present

⏺ Rank sources

└ organic docs wins downstream

⏺ Find gap

└ invite flow drops source attribution

⏺ Write report + patch

└ source-quality page and attribution fix

✓ Wrote src/pages/admin/source-quality.astro

→ localhost:4321/admin/source-quality

localhost:4321/admin/source-quality

Source quality

organic docs beats paid search after activation

Ranked by downstream behavior, not signup volume

quality

3.1×

organic docs 42% activated paid

partner newsletter 29% activated paid

paid search 13% activated paid

recommendation move budget

Paid search produces signups, but docs traffic produces customers. The agent also found source attribution missing after invite acceptance.

Did this feature improve retention?

cursor — ~/myapp

> Two months ago we released shared projects. Did it improve retention or reduce churn?▎

I'll find the feature release, compare cohorts before and after launch, separate adopters from non-adopters, and write the retention readout.

⏺ Find release

└ shared_projects shipped 8 weeks ago

⏺ Build cohorts

└ pre-launch vs post-launch paid accounts

⏺ Compare retention

└ day-60 retention up 12pp for adopters

⏺ Write readout

└ retention report with adoption recommendation

✓ Wrote src/pages/admin/shared-projects-retention.astro

→ localhost:4321/admin/shared-projects-retention

localhost:4321/admin/shared-projects-retention

Feature impact

shared projects improved retention for teams that adopted it

Cohorts, feature usage, and churn movement in one readout

retention

+12pp

before launch 34% day-60 retained

after launch 41% day-60 retained

adopted shared projects 46% day-60 retained

did not adopt 31% day-60 retained

readout

The feature appears to reduce churn when teams create at least two shared projects. The next move is to push adoption earlier in onboarding.

Start in the repo. End with an answer your agent can act on. See more examples ->

Why this exists

The interface changed.

Modern observability tools were designed for humans clicking dashboards. AI agents change the interface.

Agents can:

▸ build customized views
▸ automate operational workflows
▸ generate integrations
▸ analyze trends and suggest improvements
▸ investigate incidents and fix bugs

Agentry is designed from the ground up for agentic software development.

Features

What's in the box.

One API for the production signals your agent needs.

ONE EVENT STORE

Errors, analytics events, and deploys in one project. One key. One query surface.

PLAIN HTTP API

No SDK. Your agent adds a small helper in the repo and sends fetch calls.

CASES

Similar errors group by fingerprint, with status, frequency, affected users, and examples.

DEPLOY ATTRIBUTION

CI posts each release so failures and behavior shifts can point back to a change.

QUERYABLE EVENTS

Ask about activation, churn, campaigns, or regressions using real event data.

WEBHOOKS

Signed hooks for cases, deploys, and events when your app needs to react.

Pricing

Simple pricing.

Every plan includes errors, analytics events, deploys, and agent-readable APIs. Pick by volume and retention.

Free

$0/mo

50k events/mo
90-day retention
For trying Agentry on a real project.

Pro

$39/mo

1M events/mo
180-day retention
For active products using agents daily.

Scale

$149/mo

10M events/mo
365-day retention
For higher-volume teams and studios.

FAQ

Quick answers.

Who is Agentry for?

Teams shipping software with AI coding agents. Best fit: repo access, real users, and product or reliability questions.

Do I need repo access?

Yes. Your agent needs the repo to inspect code, wire signals, and open reports or fixes.

Do I need to be an engineer?

Not by title. But the workflow is technical, and your agent needs code, API, and deploy access.

What data does it use?

Errors, analytics events, and deploy records that your app or CI sends over HTTP.

Does it replace Sentry or PostHog?

It can sit beside them or replace the parts you do not need. Agentry gives your agent one context layer.

Is there a dashboard?

No built-in dashboard. Your agent queries the API and can write reports, pages, or PRs in your repo.

Which agents work?

Codex, Claude Code, Cursor, Windsurf, Cline, or any coding agent that can read docs and call HTTP.

What does it cost?

Free includes 50k events/month. Pro is $39/month for 1M. Scale is $149/month for 10M.

Get started

Give your agent production context.

One place to query errors, analytics events, deploys, answer contracts, and project context from real production data.

Install https://agentry.sh/install.md for me

+ Full access

5.5 Extra High

1. Open your repo in Codex, Claude Code, Cursor etc.
2. Paste the install prompt.
3. Your agent reads the install doc and shows you an implementation plan for approval.