{"slug": "show-hn-we-gave-ai-coding-agents-production-memory", "title": "Show HN: We Gave AI Coding Agents Production Memory", "summary": "Codela launched a production memory layer for AI coding agents that checks each deploy against observability data from SigNoz, Datadog, Sentry, New Relic or Grafana and writes the resulting lessons into a repo's .codela/lessons.md file, which Claude Code, Cursor and Codex read before writing code. On connection, Codela reviews the last 30 days of deploys and a year of Git history, and each lesson arrives as a pull request; the tool only writes its own lesson files, never the user's code, and observability keys are encrypted and read-only. The company cites a case where calling get_stock() once per cart item made checkout 4× slower after deploy a1b2c3d, turning one checkout request into 60+ calls to inventory-service, and its fix is a single get_stock_many() call in batches of 50 or fewer.", "body_md": "# Production memory\n\nfor AI coding agents.\n\nYour AI coding agents ship code fast. Codela checks every deploy against how your app actually behaves in production, learns from the ones that broke it, and teaches your agents so they don’t repeat the mistake.\n\n## See what Codela learns from your repo\n\nEvery revert and production fix in your history is a mistake your agents could make again. Codela turns them into rules they follow.\n\nPaste a public repo. Codela reads its last year of reverts and production fixes and shows the lessons your coding agents would get. Public repos only, nothing is written anywhere.\n\n## Tests pass. Production breaks. \n\nThe agent never finds out.\n\n$ npm test\n\n✓ 24 tests passed\n\n$ git push\n\n→ deployed to production\n\n01\n\n### Tests pass\n\nYour coding agent writes code and runs the tests locally. They pass, so it ships.\n\nKeyError: 'sku' ×37\n\np95 120ms → 480ms\n\n02\n\n### Production breaks\n\nThen production shows what the tests couldn't: a slow page, a new error, too many calls to another service.\n\nClaude Code\n\nno memory of this\n\n03\n\n### The agent never finds out\n\nSomeone on your team fixes it, but the agent never learns what went wrong, so it makes the same mistake again next time.\n\n.codela/lessons.md\n\n+ Batch inventory calls:\n\n+ one per cart, not per item\n\nread by your agents\n\nWith Codela\n\n### The lesson reaches the agent\n\nCodela turns what happened in production into lessons your agents read before their next code change, so the same mistake doesn't ship twice.\n\nGet started\n\n## Up and running in minutes\n\nSign in with GitHub\n\nPick the repos Codela should watch. It reads code and deploys, comments on pull requests and adds lessons to your repo. It only ever writes its own lesson files, never your code.\n\nConnect your observability tool\n\nPick SigNoz, Datadog, Sentry, New Relic or Grafana and paste a read-only key. Codela checks it works and matches your repos to their services. Keys are encrypted.\n\nLessons go into your repo\n\nRight after you connect, Codela checks your last 30 days of deploys and a year of Git history, so you can see what it would have caught before you rely on it. From then on every deploy is checked automatically. Each lesson arrives in your repo as a pull request, and Claude Code, Cursor and Codex read it before they write code. Want to check every lesson first? Switch to review before merge in Settings.\n\n## Using Claude Code on your computer? Add the plugin to get lessons instantly. Show commands\n\n```\n/plugin marketplace add Subomi1234/codela-plugin\n/plugin install codela@codela\n/codela:setup\n```\n\nMemory layer\n\n## Your AI agent reads the memory before it writes\n\nEvery production problem becomes a lesson. Your agents read the lessons before they write code.\n\nIn Claude Code\n\n⎿ Codela: 3 production lessons for acme/checkout loaded\n\n> Show stock levels next to each item in the cart\n\n● Before I write this: Codela has a production lesson for this repo. Calling get_stock() once per cart item made checkout 4× slower after deploy a1b2c3d.\n\nSo I'll fetch every item's stock in a single get_stock_many() call, in batches of 50, and pass the results into the cart view.\n\n⎿ Edited checkout/cart.py (+18 −3)\n\nOn pull requests\n\nCodela: production lessons\n\nThis PR looks like it repeats a mistake this repo already made in production.\n\nCalling inventory-service once per item made checkout 4× slower\n\n**Where:** `checkout/cart.py` line 42: `get_stock()` is called inside a loop over the cart items\n\n**Learned from:** p95 latency 410ms → 1.7s after deploy `a1b2c3d`\n\n**Why:** one checkout request turned into 60+ calls to inventory-service.\n\n**Ways to fix:**\n\n- Fetch all items in one call with `get_stock_many()`\n- Keep batches at 50 items or fewer; the service rejects larger ones\n\nOnly lessons learned from this repo's own production data are checked. Codela\n\nYour dashboard\n\n## Lessons Codela learned from production, all in one place.\n\nSee every deploy Codela checked, what broke and why, and which changes came from AI agents. Invite your team: everyone's agents share the same lessons.\n\nDeploys checked\n\n31\n\n3 got worse\n\nLessons in your repo\n\n5\n\nread by your agents\n\nPull requests warned\n\n2\n\nrepeated a mistake\n\nLatest lessons\n\n- highBatch inventory calls: one request per cart, not per itemAI-written · seen 3×\n- mediumSample tracing at 10% when a second tracer runsAI-written\n- mediumRetry refunds only with an idempotency keyseen 2×\n\nActivity\n\n- 4f1c2e9 · Got worse · new error after deploy: KeyError: 'sku' ×37\n- b7d03a1 · Fine: no regression\n\nWorks with your editor, your AI agent, and your APM\n\n[See all integrations](https://usecodela.com/integrations)\n\nIn our tests\n\n## Benchmarks\n\nWe tested Codela on Codela\n\nClaude Code · 8 runs each · repeated the mistake\n\n8 → 0\n\nTurning on Sentry made Codela 3× slower (p95 16ms → 54ms), so Codela wrote a lesson. Then we asked Claude Code to add Datadog. Without the lesson it made the same mistake 8 out of 8 times. With Codela, never.\n\nLimits the code doesn't show\n\n3 scenarios · 20 runs in total · repeated the mistake\n\n20 → 0\n\nExample: a stock service that refuses requests with more than 50 items. The code doesn't say so. Across three limits like this, Claude got it wrong 20 out of 20 times. With Codela's lessons, never.\n\n## Questions teams ask before they start.\n\n## How is this different from Sentry or Datadog alerts?\n\nAlerts tell a person that something broke. Codela works out which code change caused it and turns that into a lesson your coding agents read, so the agent doesn't write the same thing again. It uses the monitoring tool you already have; it doesn't replace it.\n\n## Do I have to wait for my next deploy to see anything?\n\nNo. When you connect, Codela checks your last 30 days of deploys and reads a year of your Git history (reverts and production fixes), so you see what already broke production, which of those changes came from AI agents, and the lessons it would have written. You can review each lesson before it goes into your repo.\n\n## What happens if someone repeats a past mistake?\n\nCodela comments on the pull request with the lesson and the production numbers behind it, so the mistake is caught in review instead of in production.\n\n## Can't I just write lessons in CLAUDE.md myself?\n\nYou can, but almost nobody does it after every incident, and first you have to work out which change caused the problem. Codela does both automatically after every deploy, with the numbers to back it up.\n\n## What if Codela gets a lesson wrong?\n\nIt only writes a lesson when it finds that the code change caused the problem, not just that the timing lined up. You can delete any lesson from your dashboard, and it's removed from your repo too.\n\n## Does Codela change my code?\n\nNever. The only thing it writes is its lesson files (.codela/lessons.md, plus a line in CLAUDE.md and a section in AGENTS.md), through pull requests. You can turn that off in Settings.\n\n## What data does Codela see?\n\nThe code changes in the repos you pick, and read-only data from your monitoring tool: speed, error rates, error messages and traces around each deploy. Keys are checked live and stored encrypted, and your lessons are never shown to anyone outside your workspace.\n\n## How much does it cost?\n\nFree for 1 repo and 20 pull request checks a month, no card needed. Pro is $7.99 per person per month for unlimited repos, pull request checks and your whole team. Cancel anytime.", "url": "https://wpnews.pro/news/show-hn-we-gave-ai-coding-agents-production-memory", "canonical_source": "https://usecodela.com/", "published_at": "2026-11-09 17:31:05+00:00", "updated_at": "2026-11-09 17:53:06.776873+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "developer-tools", "mlops", "ai-products"], "entities": ["Codela", "Claude Code", "Cursor", "Codex", "SigNoz", "Datadog", "Sentry", "New Relic"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/show-hn-we-gave-ai-coding-agents-production-memory", "markdown": "https://wpnews.pro/news/show-hn-we-gave-ai-coding-agents-production-memory.md", "text": "https://wpnews.pro/news/show-hn-we-gave-ai-coding-agents-production-memory.txt", "jsonld": "https://wpnews.pro/news/show-hn-we-gave-ai-coding-agents-production-memory.jsonld"}}