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Your AI agents work in demo.
We get them to production.
A Kubernetes-native runtime for AI agents. Retry, resume, approval gates, audit trails, autoscaling. Managed by your DevOps team.
Your agent works. The infra to run it doesn't exist yet.
Retries from scratch → Resumes from the failed step
No decision log → Structured audit trail
No human in the loop → Approval gates before action
Breaks at 10x volume → Autoscale 2–200 on queue depth
Every team deploys differently → One runtime, kubectl
"We spent six months building agent infrastructure. Switched to Hatch and had production agents running in a week."
Director of Platform Engineering, Series C energy company 84% of engineering time on agent projects has nothing to do with the agent.
Where teams actually spend time
on agent projects.
Retry / failure handling 24%
Observability / logging 18%
Scaling infrastructure 16%
Approval workflows 14%
Compliance / audit 12%
Actual agent logic 16%
84% of the work has nothing to do with the agent itself. Hatch handles all of it.
How it works
Your code Agent + hatch.yaml Python, TypeScript. Steps, failure policy, scaling, gates.
Your infra Kubernetes + Prometheus Your cluster, your dashboards, your on-call.
↓ hatch deploy ↓
Hatch Runtime
Retry / resume Approval gates Audit trails Autoscaling Observability Failure recovery
apiVersion: hatch.run/v1 kind: Agent metadata: name: claims-processor namespace: production spec: goal: "Process insurance claims end-to-end" steps: - ingest: "Receive claim from queue" - analyse: "Extract fields, validate documents" - decide: "Run underwriting rules" approvalGate: true # human signs off - payout: "Trigger disbursement" failurePolicy: learn-and-retry resumeFrom: last-successful-step scaling: min: 2 · max: 200 · metric: queue-depth observability: logs: structured · metrics: prometheus · alerting: pagerduty
Six industries. Same problem.
"Why not Temporal?"
Temporal orchestrates workflows. Hatch runs agents. Agents make decisions, need human approval mid-step, fail in ways that require step-level resume, and produce audit trails regulators inspect. You could build this on Temporal. It takes 4–6 months. Hatch does it in two weeks.
What you get in two weeks
01 Agent in production Your agent deployed and running on Hatch, handling real workload.
02 Failure recovery Step-level retry and resume, tested under production load.
03 Approval gates Human sign-off configured on critical agent decisions.
04 Audit trails Structured logs feeding into your existing monitoring stack.
05 Autoscaling Tested at production volume. 2 to 200 on queue depth.
06 Performance report Written assessment with metrics and recommended next steps.
FAQ
Do we need to have an agent already built?
Yes. Hatch is not an agent-building platform. We take agents your team has already built — in Python, TypeScript, or any language — and give them the infrastructure to run reliably in production. If you don't have an agent yet, we're not the right fit.
We're already on Kubernetes. How does this fit in?
Hatch runs on your existing Kubernetes cluster. It's not a separate platform — it's a runtime layer that your DevOps team manages with kubectl, Helm, and the tools they already know. We add agent-specific primitives: step-level retry, human approval gates, structured audit logs, and autoscaling based on agent workload metrics.
What happens when an agent fails mid-workflow?
Hatch tracks agent progress at the step level. When a failure occurs — an API timeout, a model error, a resource limit — the agent stops, logs the failure with full context, and resumes from the last successful step when the issue is resolved. No reprocessing. No lost state.
How is this different from just running agents on AWS or GCP?
You can run containers on AWS. You can't run agents. Agents make decisions, fail mid-workflow, need human approval, and require structured audit trails. AWS gives you compute. Hatch gives you the runtime primitives that make agent workloads production-grade — retry, resume, approval gates, observability, and autoscaling based on agent-specific metrics.
Can you run this in our private cloud or on-premises?
Yes. Hatch deploys anywhere Kubernetes runs — AWS, GCP, Azure, on-premises, or air-gapped environments. The enterprise tier includes on-prem deployment support and dedicated infrastructure configuration.
What does the 2-week PoC actually produce?
A single agent, running in production on Hatch, handling real workload. You get a deployed agent with step-level observability, failure recovery, and audit logging — plus a written report covering performance metrics, failure handling, and a recommended path to full platform deployment.
Pick one agent. Two weeks.
If it works, we keep going. If it doesn't, you stop. No multi-year contracts.
book a call →