amjadfatmi1

I’m seeing more teams ship agentic systems that can call real tools (DB writes, deploys, email, billing, internal APIs). Most of the safety patterns I hear are prompt rules + basic validation + “human-in-the-loop for risky stuff.”

My question: in a real production environment, what’s your enforcement point that the agent cannot bypass? Like, what actually guarantees the tool call isn’t executed unless it passes policy?

Some specific things I’m curious about:

Are you enforcing permissions inside each tool wrapper, at a gateway/proxy, or via centralized policy service?

How do you handle identity + authorization when agents act on behalf of users?

Do you log decisions separately from execution logs (so you can answer “why was this allowed?” later)?

How do you roll out enforcement safely (audit-only/shadow mode -> enforcement)?

What failure modes hurt most like policy bugs, agent hallucinations, prompt injection, or tool misuse?

Would love to hear how people are doing this in practice (platform/security/infra teams especially)

I've been experimenting with autonomous agents that have shell and database access. The standard approach seems to be "put safety guardrails in the system prompt", but that feels like a house of cards honestly. If a model is stochastic, its adherence to security instructions is also stochastic.

I'm looking into building a hard "Action Authorization Boundary" (AAB) that sits outside the agent's context window entirely. The idea is to intecept the tool-call, normalize it into intent against a deterministic YAML policy before execution.

A few questions for those building in this space:

Canonicalization: How do you handle the messiness of LLM tool outputs? If the representation isn't perfectly canonical, the policy bypasses seem trivial.

Stateful Intent: How do you handle sequences that are individually safe but collectively risky? For example, an agent reading a sensitive DB (safe) and then making a POST request to an external API (dangerous exfiltration).

Latency: Does moving the "gate" outside the model-loop add too much overhead for real-time agentic workflows?

I’ve been working on a CAR (Canonical Action Representation) spec to solve this, but I’m curious if I'm overthinking it or if there’s an existing firewall for agents standard I'm missing.

I spent the last few months watching LLM agents "vibe-code" their way into production disasters. We’ve been pretending that a 500-word system prompt is a security layer. It isn't. It's just a polite request that the model is one jailbreak away from ignoring.

I built Faramesh because I wanted a hard, cryptographic boundary between the agent's "brain" and my actual infrastructure. It intercepts tool-calls and forces them through a deterministic gate before any code runs. If the action isn't in your policy, it simply doesn't exist.

The biggest headache was canonicalization.py. LLMs are messy—one model sends a float as 1.0, another as 1.00, and it breaks the cryptographic hash every time. I wrote a normalization engine to ensure that identical intent produces the exact same byte-stream and hash.

It’s open source (Python/Node SDKs). I’m curious if people think this should live at the framework level or as a standalone proxy. Tear the code apart:

https://github.com/faramesh/faramesh-core

For theory lovers, I'd invite you to read a paper i published just recently titled "Faramesh: A Protocol-Agnostic Execution Control Plane for Autonomous Agent systems" (Link below)

https://doi.org/10.5281/zenodo.18296731