AGNT5

The orchestration platform for AI workflows and agents

AI workflows break easily — not because developers lack skill, but because the problems are distributed, stateful, and long-running.

When an LLM call fails 4 minutes into a 5-minute workflow, recovery isn’t just a retry — it requires replay semantics, checkpoint recovery, and idempotent execution. When two agents race over shared context, you need deterministic scheduling and state isolation. When you want to improve a prompt, you need to replay historical runs to measure the change.

AGNT5 is a durable execution engine that solves these problems.

With automatic state management, checkpointing, and replay, AGNT5 handles the hard distributed systems challenges — so you can focus on building workflows, not managing their lifecycle.

What makes AGNT5 different?

  • Durable execution at the core Every workflow automatically checkpoints its state. When failures happen — LLM timeouts, API errors, process crashes — workflows resume exactly where they left off. No manual recovery code. No lost progress.

  • Polyglot from day one Python, TypeScript, Go, and Rust SDKs with identical APIs. All SDKs expose the same abstractions over the same execution engine. Migrate a workflow from Python to TypeScript or Go to Rust without changing its logic — just translate syntax. No feature gaps, no language-specific quirks.

  • Replay as a primitive Every execution is reproducible by version hash. Re-run historical workflows with updated prompts, models, or code. Measure improvements. Debug production issues locally.

  • Native observability OpenTelemetry instrumentation built into the execution engine. AGNT5’s traces automatically connect to your application’s traces — from the API request that triggered the workflow, through workflow logic and state transitions, to every LLM call and tool invocation. True end-to-end distributed tracing across your entire stack, not just workflow internals.

  • No DSLs or graphs Write workflows as normal code. Use functions for simple tasks, entities for durable objects with state, workflows for complex orchestration, and compose agents with tools. AGNT5 makes your code durable — you don’t rewrite it for durability.

Core Building Blocks

AGNT5 is built on four foundational abstractions that together make AI workflows reliable, reproducible, and composable.

Functions do work → Entities hold state → Workflows orchestrate → Agents reason.

  • Function — the atomic unit of execution Any function can run directly within AGNT5. Functions are pure, retryable, and isolated — the smallest building block of execution.

  • Entity — durable state manager Entities encapsulate application state with single-writer guarantees. They persist automatically and provide consistent reads and writes across runs.

  • Workflow — orchestrator of steps and entities A workflow coordinates multiple functions and entities. Each step is checkpointed, resumable, and fully traceable.

  • Agent — autonomous orchestrator built on workflows Agents use workflows and entities to reason, plan, and act. They can coordinate tools and other agents while maintaining context and goals.

Together, these layers form the foundation of AGNT5’s model of computation: durable, deterministic, and observable by default.

When to use AGNT5?

Use AGNT5 when you’re building:

  • Multi-step AI workflows that can’t lose progress on LLM timeouts or API failures
  • Multi-agent systems that need safe coordination over shared state
  • Production workflows that require reproducibility for debugging or evaluation
  • AI applications where you need to measure prompt/model improvements against real execution history

Next steps