Aegis-control

What it does

AI agents don't just generate text anymore — they call tools. They query databases, hit APIs. And right now, almost nobody is governing what those tools can actually do. Aegis Control sits in your LLM request path and enforces policies on tool calls — before they execute. Platform and security teams shipping LLM agents to production who need more than a dashboard. If you're running agents with real tool access, and you need to control and audit what those tools can do, Aegis is for you.

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  Hi HN, Matvey, Ildar, Joey, and Dominik here. If you're building LLM agents that use tools, you're probably worried about prompt injection attacks that can hijack those tools. We were too, and found that solutions like prompt-based filtering or secondary "guard" LLMs can be unreliable. Our thesis is that agent security should be handled at the network level between the agent and the LLM, just like a traditional web application firewall. So we built Archestra Platform: an open-source gateway that acts as a secure proxy for your AI agents. It's designed to be a deterministic firewall against…

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  AegisureJun 2026 · aegisure.dev · ▲5

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  Limits – Control layer for AI agents that take real actionsFeb 2026 · limits.dev · ▲9

  Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard. Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement. Three modes: Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc One line to integrate: npm…

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  Aegis – A framework for AI-governed software development2025 · github.com · ▲5

  Hey HN – I built a framework called Aegis to govern AI-assisted software development. The core idea is that AI-generated code should follow the same rules as human code: versioned, validated, observable. Aegis enforces this through blueprint-based development, drift detection, and runtime compliance systems. It’s designed for teams using tools like Copilot, Kilo, or Lovable to build production systems with confidence. This isn’t a library — it’s a way to architect AI-native engineering workflows. Would love feedback, questions, and critiques. Especially curious if others are facing similar…

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