Most people are focused on making AI agents smarter. But what happens when those agents need to actually do things reliably?
That’s where Hatchet comes in.
@hatchet_dev is building the execution layer for AI agents and complex backend workflows.
Think about an agent that needs to:
→ call multiple tools
→ process thousands of documents
→ run tasks in parallel
→ wait for human approval
→ retry failed operations
→ survive crashes
→ continue exactly where it stopped
That gets messy very quickly if you’re building all the infrastructure yourself.
Hatchet handles the orchestration.
It provides background tasks, retries, scheduling, durable execution, workflow orchestration, concurrency controls and observability in one platform.
It’s also open-source, supports Python, TypeScript, Go and Ruby, and can be self-hosted or used through Hatchet Cloud.
And I think the AI-agent angle is where this gets particularly interesting.
AI agents are moving from simple demos to long-running systems that need to operate reliably in production.
The smarter the agents become, the more important the infrastructure underneath them becomes.
If you’re a developer building AI agents, background processing, data pipelines or distributed applications,
Hatchet.run is definitely something worth having on your radar.