Production intelligence for AI-generated code.

Teams ship by feature. The operational stack still speaks service. When checkout breaks, the user doesn’t care which microservice failed — but every dashboard makes you do that translation by hand.

More of the code is being written by agents that don’t read it afterward. Service boundaries mean nothing to them. The feature is the only abstraction that actually exists for the team operating it.

praetom reads your repo · names your features · instruments them

1. A

   A feature contract, in plain English.

   You write what the feature does, what counts as healthy, what an incident looks like. “checkout succeeds within 400ms for 99.9% of users.”

2. B

   A feature map, built by AI.

   praetom reads the codebase and traces every path that implements the contract — across services, queues, databases, frontend, external APIs. The graph updates as the code changes.

3. C

   A consultable production reality.

   On top of the graph: code, telemetry, incidents, narrative. Coding agents query it before generating. The PR bot queries it on diff. You query it in Slack. Same call, structured data for the agent + a rendered widget for you.

The LLM is the new primary writer of code. Shift-left reaches its asymptote at the moment of generation. Nothing is earlier.

praetom doesn’t add a tab. It lands in the tools your team is already in. One MCP server backs Claude, Cursor, and ChatGPT; one Slack app handles the team-broadcast slot. Same answer everywhere.

→ praetom.what_changed("checkout")

{
  "since": "30d",
  "prs": [
    { "id":1247, "by":"@dani" },
    { "id":1233, "by":"@miles" },
    { "id":1221, "by":"@dani" }
  ]
}

47async function processRefund(req) {
48  ↳ consulting praetom...
    similar to: checkout
    existing pattern: line 47
    avoid: retry loop (#1233)
49  await validateRefund(req);
50}

---
name: praetom
description: feature health,
  usage, incidents, ownership
when_to_invoke: feature
  mentioned by name
---

Four independent technical shifts had to land. All four did, in early 2026.

LLMs that can read a codebase and trace call graphs.

OpenTelemetry at critical mass — feature tags propagate cleanly across services.

MCP as the standardized agent integration surface — install once, every agent gets access.

MCP rich-UI just shipped — the same tool call serves a JSON return for the agent and a rendered widget for the human.

vs. Sentry / Datadog

Their primitive is the service; their SLO is a latency budget. Their tools assume a code-literate user. praetom’s primitive is the feature; the contract is plain English.

vs. PostHog

Frontend-event-centric. praetom instruments backend along the same feature boundaries the frontend events already mark.

vs. Bits AI / Seer / Resolve

Reactive. They answer “what just broke.” praetom answers “what’s about to break” — before generation.

vs. Vercel Agent

Platform-locked to Vercel-hosted apps. praetom runs on Railway, Fly, AWS, GCP, self-hosted.

One MCP server backs Claude, Cursor, and ChatGPT. One Slack app handles the team-broadcast slot. One GitHub app drops the PR check into your repo. Same model behind all of them.

Onboarding teams by hand so the first month is right. Sign up and we’ll be in touch with next steps.