Multi-agent orchestration platform. The infrastructure layer for AI agents that actually need to work in production — with lifecycle management, observability, and governance built in from day one.
Agent Taskflow treats AI agents the way enterprises treat employees and legacy systems — monitored, governed, auditable, and accountable. Not just prompts with tool access. Agents with identity, lifecycle, and operational history.
Built on an event-driven Kafka architecture with 18+ microservices, ATF implements a cognitive lifecycle for every agent — from initialization through suspend/resume, feedback loops, and graceful shutdown. Every decision is logged. Every action is auditable.
Suspend/resume with consensus feedback patterns. Agents don't just run — they think, pause, reflect, and continue with full state preservation.
Event-driven microservices architecture. Every agent action, decision, and state change flows through the bus for real-time processing and replay.
Every agent has identity, permissions, audit trails, and operational boundaries. Monitored like humans and legacy systems, not black boxes.
Every decision logged. Every tool call traced. Every cost attributed. Complete observability from agent inception through shutdown.
Agents operate within defined trust boundaries. Role-based access, scoped permissions, and sandboxed execution environments.
Supports multiple agent architectures — from large LLM-backed agents to small RWKV specialists. The platform, not the model, is the product.
ATF is the infrastructure layer for AI agents that need to work in production.
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