The infrastructure patterns behind every successful AI agent deployment.

The moat is not access to models. It is the ability to redesign workflows, keep humans at the right boundary, and run agents reliably enough that the organization actually learns.

Resume from failures, never restart

Why it matters

Goldman Sachs doesn't re-run trade accounting from scratch when a system fails. Neither should you.

How JamJet delivers it

Every workflow step writes a checkpoint before execution. On crash, JamJet replays completed steps from the event log and continues from the exact point of failure.

Complete traceability by default

Why it matters

Regulated industries — finance, legal, insurance — require full traceability. The firms that built audit trails into their agent workflows from day one avoided the compliance headaches that catch teams who bolt it on later.

How JamJet delivers it

Every state transition, tool call, and model response is persisted as an event automatically. No manual logging, no reconstruction after the fact.

Human oversight as a first-class capability

Why it matters

Klarna's public reversal made this lesson impossible to ignore: human-in-the-loop cannot be bolted on after quality degrades. It must be designed in from the start.

How JamJet delivers it

Human approval is a first-class node type. Your workflow pauses, presents the decision to a human, and resumes when they approve — with the approval logged in the audit trail.

Route to any model, no vendor lock-in

Why it matters

Goldman routes across OpenAI, Gemini, and Llama by task type. Model flexibility is how you avoid vendor dependency and optimize cost.

How JamJet delivers it

Models are interchangeable resources. Route to Claude for reasoning, Gemini for code generation, and a local model for sensitive data — in the same workflow.

Standard protocols, not proprietary walls

Why it matters

The industry is converging on standard protocols. Agents that cannot interoperate are agents that cannot scale beyond your own walls.

How JamJet delivers it

Native support for Model Context Protocol (MCP) and Agent-to-Agent Protocol (A2A). Your agents collaborate with agents in other frameworks, other organizations, other runtimes.

Rust core

No garbage collector, real parallelism, checkpoint writes at native speed.

Event-sourced by default

Durability is the execution model, not an add-on.

Protocol-native

MCP + A2A built into the runtime, not wrapped around it.

Open source

Apache 2.0. No vendor lock-in.

Try it now

$ pip install jamjet
Read the quickstart →

See it in action

Walk through a production architecture — multi-agent coordination, human oversight, and durable execution.

See the showcase →