kaizen

To build production-ready multi-agent pipelines, use signature-based programming to define type-safe inputs and outputs, extend a base agent class, and coordinate specialists using semantic capability matching and shared memory pools.

Signature-based programming for AI agents defines type-safe inputs and outputs with validation, allowing you to implement autonomous agents by extending a base class and running them with structured execution patterns.

Add checkpoint resume to long-running AI workflows by using enterprise-grade autonomy primitives that support built-in memory systems, cost tracking, and observability to safely pause and recover execution state.

Yes, you can build multimodal RAG agents with vision and audio capabilities by extending the base agent with multimodal tools, integrating retrieval pipelines, and orchestrating them through multi-agent coordination patterns.

The best way to orchestrate multiple AI agents is using a unified framework that provides shared memory pools, semantic capability matching, and structured coordination patterns like router, supervisor, ensemble, or consensus.

Yes, YAML frontmatter in a SKILL.md file is required for discovery, alongside signature definitions for validation, to ensure deterministic and production-focused operational requirements are met.