Runtime Discoverable Action Exposure With Access Control Policies

via “action-capability-discovery-and-negotiation”

Background: I've been working on agentic guardrails because agents act in expensive/terrible ways and something needs to be able to say "Maybe don't do that" to the agents, but guardrails are almost impossible to enforce with the current way things are built.Context: We keep

Unique: Treats action discovery as a first-class concern with explicit capability negotiation rather than assuming all agents have access to all tools, enabling fine-grained permission models and dynamic tool registration

vs others: More flexible than static action lists and more secure than MCP's open-ended tool exposure because agents only see actions they're authorized to use

via “runtime-discoverable action exposure with access control policies”

A fast and minimal framework for building agentic systems

Unique: Combines runtime action discovery with declarative access policies via @action decorator, enabling agents to expose capabilities that are both discoverable and access-controlled without requiring centralized registries or pre-shared schemas

vs others: More flexible than OpenAI function calling (which requires schema pre-definition) because actions are discovered at runtime; more minimal than LangChain tools because it doesn't require tool definitions or JSON schemas upfront

via “policy-driven tool access control with dynamic permission evaluation”

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Unique: Implements a declarative policy engine with attribute-based access control (ABAC) that evaluates complex conditions (time-based, context-aware, rate-limiting) at request time, with in-memory caching to minimize latency while supporting dynamic policy updates

vs others: More expressive than simple RBAC (which only considers roles) and more efficient than evaluating policies in external systems, enabling complex access rules without sacrificing performance