The Agent Engine
Infrastructure purpose-built for autonomous AI agents. Durable execution, full observability, cost control, and multi-agent orchestration — in one platform.
Traditional infrastructure wasn't built for agents
Agents aren't stateless HTTP handlers. They run for hours, make hundreds of decisions, call external APIs, and need to recover from failures without losing progress.
Serverless functions time out. Container orchestrators don't checkpoint agent state. Observability tools weren't designed for LLM decision traces.
The Agent Engine was designed from the ground up for agent workloads.
Built for agent workloads
Durable Agent Execution
Checkpoint and resume agents across crashes, deployments, and restarts.
Execution Tracing
Automatic capture of every tool call, LLM query, and decision point.
Replay & Debugging
Step through any historical run to understand agent behavior.
Cost Guardrails
Per-agent budgets, token limits, and spending policies.
Stateful Workflows
Durable state management across long-running agent tasks.
Multi-Agent Orchestration
Route tasks, share context, and manage dependencies between agents.
Tool & API Integration
Unified tool framework with retry, rate limiting, and auth management.
Model-Agnostic Architecture
Swap LLM providers without rewriting agent logic.
How it fits together
The Agent Engine sits between your agent code and the infrastructure it runs on. It manages execution state, observability, and resource control — so you focus on agent logic.
Architecture Diagram