ai-agents

Auditable AI agents are built by extending base supervisor and subagent classes, enforcing single-writer state models and EventBus routing to ensure deterministic, compliant state transitions across multi-agent workflows.

EventBus routing in multi-agent orchestration centralizes communication by directing agent interactions through a single channel, preventing lateral agent calls and maintaining deterministic workflow execution.

Guardrails and audit compliance in AI agent workflows are enforced through dynamic testing contracts, strongly typed Pydantic v2 inputs and outputs, and cost tracking within a meta dictionary across all agent layers.

Yes, Pydantic v2 is required for strongly typed inputs and outputs in this AI agent state machine framework, ensuring data validation and structural integrity across supervisor, agent, and subagent components.

A single-writer state model is required in multi-agent AI workflows to prevent race conditions and unauthorized state mutations, ensuring that only designated components modify state transitions for reliable auditing.

Preventing lateral calls in AI agent orchestration limits direct peer-to-peer communication, requiring all interactions to route through the EventBus and supervisor layers, which ensures strict auditability but restricts ad-hoc agent messaging.