This is the central challenge of moving agents from single-player demos to multi-player production systems. My team has been tackling this, and we've landed on a few patterns that work well.

1. For policies (what agents can/can't do): We've had a lot of success with an in-process, "pre-execution hook" model. Instead of having the agent's core logic be full of if/else permission checks, we use a lightweight policy engine. Before any critical action (like a tool call), the agent's intent is passed to the engine, which evaluates a set of simple, deterministic Python functions (our policies) against the current context (user role, budget, etc.). If any policy returns BLOCK, an exception is raised and the action is never executed. It's like a sub-millisecond, framework-agnostic version of OPA that doesn't require a sidecar.

2. For managing shared data & mixing frameworks: The key insight for us was to treat this as a classic observability problem. The only way to debug race conditions or interoperability failures between a LangGraph agent and a CrewAI agent is to have a unified, high-fidelity trace of the entire system.

We built a simple tracing system with a @trace decorator that we can apply to functions in any framework. All traces are written to a single, local SQLite database. This gives us a "global view" of the system. When data gets corrupted, we can run a SQL query like SELECT * FROM spans WHERE attributes->>'data_id' = 'xyz' ORDER BY start_time_ns to see the exact sequence of reads and writes from all agents that touched that data.

(For anyone interested, we've open-sourced this entire approach as a single toolkit called Clearstone. The repo is in my profile. It's in an early beta, but it has the policy engine and the local SQLite tracer we're using.)