Writing

'What if the AI does something bad' is too vague to build a security program around. Here are the four attack patterns that actually show up in agent deployments, and the controls that address each one.

Auditors don't ask about your model's benchmark scores. They ask who did what, when, under what authority, and whether you can prove it. Here's how AGF's audit artifacts map to the three frameworks compliance teams are actually being asked about.

Most 'AI security' conversations are still about model safety — jailbreaks, hallucinations, prompt injection. That's not the same problem as securing what an agent is authorized to do once it's deployed. Here's the distinction, and the control surface that matters.

The Model Context Protocol standardizes how agents discover and call tools. It doesn't standardize whether they should be allowed to. Here's how we think about applying agent authorization to MCP — and where we are today.

AutoGen's function_map has no authorization concept — any registered function is one tool call away from execution. Here's how to gate it with the AGF Python SDK, no framework-specific adapter required.

CrewAI's Agent/Task/Crew model has no built-in concept of policy enforcement — every tool call an agent makes is implicitly trusted. Here's how to gate CrewAI tool calls with AGF in a few lines.

The Python SDK for the AGF Authorization Service is now published on PyPI. One package, three ways to call it: a synchronous facade, an async client, and framework adapters for LangChain and CrewAI.

Multi-agent systems inherit a centuries-old problem: how do you ensure that what the subordinate does is what the principal actually authorized? This isn't a technical problem — it's a governance one.

LangChain makes it easy to build capable agents. But capability without authorization is a liability. Here's how to add policy enforcement to your LangChain agents in under 10 minutes.

Why there's no single revocation state, and why that's okay.

Everyone is talking about AI governance. But agent governance is a specific discipline within that — one most organizations aren't ready for yet. Here's what it actually means and why it's different.

Why agent governance needs a new model for revocation.

Most organizations deploying AI agents today cannot answer a simple question: what did the agent do, and why? This post explains why that's the case and what a real audit trail looks like.

The API keys and service accounts we use for human-facing systems don't translate cleanly to agent systems. Here's why, and what we need instead.