Production that runs itself.
Antimetal is the autonomous system for production. Continuously understanding, operating, and improving your environment.
You may be able to build software rapidly, but if people can’t depend on it, if you can’t figure out what breaks, well, people aren’t going to use it.”
Production engineering, as practiced today, is breaking. Software systems have grown too complex for humans to manually operate, and the knowledge required to run them is fragmented across tools, infrastructure, alerts, and tribal knowledge. The result is pages at 3 a.m., dashboards that surface symptoms instead of causes, and fixes that never become prevention.
Antimetal is building the autonomous system for production: a new layer between your team and your running systems.
It diagnoses. It fixes. It prevents. It learns how your systems run and operates production for you.
A new layer of the stack
Antimetal is the autonomous layer between your team and your production systems.
Production should run itself.
Production is too complex to run manually. Engineers should set direction, ship product, and approve important changes. The rest should be handled autonomously.
A layer that owns the runtime.
At its core sits a live world model, a continuous understanding of how your stack behaves. On top, an army of specialized agents acts on the model to diagnose, fix, prevent, and answer any question.
Everyone else watches. We operate.
Most software stops at recommendations and assistance, keeping humans in the loop as the operational layer. Antimetal is designed to continuously investigate, operate, and improve production systems itself.
Your Team
Defines priorities, direction, and goals.
Production
Runtime systems, infrastructure, code execution, and everything around them.
Production Looks Stable.
Reverted a91c3f2 after canary p95 climbed +180 ms across checkout pods.
Ticket #4192 routed to billing-oncall with payment-edge traces attached.
Idempotency gap across 6 services linked to duplicate order retries after deploy.
Opened PR #2812 with pgbouncer pool guard and rollback notes.
Bundled 4 auth tickets under one session-store regression for review.
Black-Friday warm-up at 2.4× baseline flagged cart write latency before traffic peak.
Pre-scaled checkout-svc to 12 replicas and verified queue drain stayed flat.
11 PRs queued since last login, grouped by owner and risk level.
Tuned cache TTL on rec-svc and confirmed p50 latency dropped 18 ms.
Quarantined node i-09a4 after disk-fill risk crossed production threshold.
Reverted a91c3f2 after canary p95 climbed +180 ms across checkout pods.
Ticket #4192 routed to billing-oncall with payment-edge traces attached.
Idempotency gap across 6 services linked to duplicate order retries after deploy.
Opened PR #2812 with pgbouncer pool guard and rollback notes.
Bundled 4 auth tickets under one session-store regression for review.
Black-Friday warm-up at 2.4× baseline flagged cart write latency before traffic peak.
Pre-scaled checkout-svc to 12 replicas and verified queue drain stayed flat.
11 PRs queued since last login, grouped by owner and risk level.
Tuned cache TTL on rec-svc and confirmed p50 latency dropped 18 ms.
Quarantined node i-09a4 after disk-fill risk crossed production threshold.
Reverted a91c3f2 after canary p95 climbed +180 ms across checkout pods.
Ticket #4192 routed to billing-oncall with payment-edge traces attached.
Idempotency gap across 6 services linked to duplicate order retries after deploy.
Opened PR #2812 with pgbouncer pool guard and rollback notes.
Bundled 4 auth tickets under one session-store regression for review.
Black-Friday warm-up at 2.4× baseline flagged cart write latency before traffic peak.
Pre-scaled checkout-svc to 12 replicas and verified queue drain stayed flat.
11 PRs queued since last login, grouped by owner and risk level.
Tuned cache TTL on rec-svc and confirmed p50 latency dropped 18 ms.
Quarantined node i-09a4 after disk-fill risk crossed production threshold.
Reverted a91c3f2 after canary p95 climbed +180 ms across checkout pods.
Ticket #4192 routed to billing-oncall with payment-edge traces attached.
Idempotency gap across 6 services linked to duplicate order retries after deploy.
Opened PR #2812 with pgbouncer pool guard and rollback notes.
Bundled 4 auth tickets under one session-store regression for review.
Black-Friday warm-up at 2.4× baseline flagged cart write latency before traffic peak.
Pre-scaled checkout-svc to 12 replicas and verified queue drain stayed flat.
11 PRs queued since last login, grouped by owner and risk level.
Tuned cache TTL on rec-svc and confirmed p50 latency dropped 18 ms.
Quarantined node i-09a4 after disk-fill risk crossed production threshold.
Reverted a91c3f2 after canary p95 climbed +180 ms across checkout pods.
Ticket #4192 routed to billing-oncall with payment-edge traces attached.
Idempotency gap across 6 services linked to duplicate order retries after deploy.
Opened PR #2812 with pgbouncer pool guard and rollback notes.
Bundled 4 auth tickets under one session-store regression for review.
Black-Friday warm-up at 2.4× baseline flagged cart write latency before traffic peak.
Pre-scaled checkout-svc to 12 replicas and verified queue drain stayed flat.
11 PRs queued since last login, grouped by owner and risk level.
Tuned cache TTL on rec-svc and confirmed p50 latency dropped 18 ms.
Quarantined node i-09a4 after disk-fill risk crossed production threshold.
Reverted a91c3f2 after canary p95 climbed +180 ms across checkout pods.
Ticket #4192 routed to billing-oncall with payment-edge traces attached.
Idempotency gap across 6 services linked to duplicate order retries after deploy.
Opened PR #2812 with pgbouncer pool guard and rollback notes.
Bundled 4 auth tickets under one session-store regression for review.
Black-Friday warm-up at 2.4× baseline flagged cart write latency before traffic peak.
Pre-scaled checkout-svc to 12 replicas and verified queue drain stayed flat.
11 PRs queued since last login, grouped by owner and risk level.
Tuned cache TTL on rec-svc and confirmed p50 latency dropped 18 ms.
Quarantined node i-09a4 after disk-fill risk crossed production threshold.
Reverted a91c3f2 after canary p95 climbed +180 ms across checkout pods.
Ticket #4192 routed to billing-oncall with payment-edge traces attached.
Idempotency gap across 6 services linked to duplicate order retries after deploy.
Opened PR #2812 with pgbouncer pool guard and rollback notes.
Bundled 4 auth tickets under one session-store regression for review.
Black-Friday warm-up at 2.4× baseline flagged cart write latency before traffic peak.
Pre-scaled checkout-svc to 12 replicas and verified queue drain stayed flat.
11 PRs queued since last login, grouped by owner and risk level.
Tuned cache TTL on rec-svc and confirmed p50 latency dropped 18 ms.
Quarantined node i-09a4 after disk-fill risk crossed production threshold.
Reverted a91c3f2 after canary p95 climbed +180 ms across checkout pods.
Ticket #4192 routed to billing-oncall with payment-edge traces attached.
One platform.
An army of agents.
Specialized agents that own different slices of production. Composable, customizable, and fully auditable.
Patrol
Continuously watches for operational risks, regressions, and system drift.
Triage
Turns noisy production signals into structured, actionable issues.
World Model
Continuously learns how your systems and teams behave and evolve.
Agent Builder
Create custom operational agents via natural language.
Operating production with agents
What teams want to know before trusting Antimetal in production.
Antimetal builds a continuously evolving world model of your production environment by connecting to the tools you already use.
Each signal is normalized and linked to the systems, changes, and people around it, so a deployment connects to the metrics it changed, the alert it triggered, and the engineer who pushed it.
The result is persistent operational context that updates in real time and improves the longer Antimetal runs.
No. Antimetal sits on top of the observability tools you already use and uses their data to build and maintain its world model.
Observability platforms were built for humans to read dashboards and investigate alerts. Antimetal is built for a world where production operates itself.
Coding agents are good at generating code. Operating production is different. It requires continuous understanding of a live system: what changed, what depends on what, what failed before, and what is happening right now.
That understanding cannot come from a single prompt or context window. Antimetal's world model gives agents continuously updated operational context across your infrastructure, telemetry, deployments, and code.
That is what allows Antimetal to reason about production systems in ways standard coding agents cannot.
Antimetal can investigate incidents, trace failures across systems, identify likely root causes, propose fixes, open pull requests, and carry out operational workflows on your behalf.
It starts with read access to your observability, infrastructure, and code systems so it can build and maintain its world model. From there, you control what actions it is allowed to take.
By default, changes still route through your existing approval flow, whether that is a pull request, deployment pipeline, or Slack approval.
The autonomous system for production.
Take a 30 minute demo to see if Antimetal
is the missing layer in your stack.