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Everyone is focused on making AI agents more capable. I think the real problem is making them trustworthy. An autonomous agent is not just “software with prompts.” It is: - making decisions - invoking tools - accessing memory - coordinating with other agents - acting with delegated authority That changes the security model completely. Traditional software security assumed deterministic behavior and explicit control flow. Agentic systems introduce something different: probabilistic execution with dynamic planning. And that creates a dangerous gap. For example: 1. How do you verify an agent stayed within its intended authority when the execution path itself was generated at runtime? 2. How do you establish provenance across multi-agent collaboration? 3. How do you prevent: - prompt injection from becoming privilege escalation? - memory poisoning from becoming long-term behavioral drift? - tool access from turning into lateral movement across systems? The uncomfortable part is this: Most current agent architectures still treat security as a perimeter problem. But in autonomous systems, the “inside” is no longer trustworthy by default. An agent can chain tools, generate sub-goals, delegate tasks, persist memory, and interact with external systems in ways the original developer never explicitly enumerated. That starts looking less like traditional application security… …and more like securing a distributed organization of semi-autonomous actors. My current belief: The next major breakthroughs in AI agents may come less from model intelligence and more from: - Identity architectures - Execution sandboxes - Capability-scoped memory - Cryptographic provenance - Policy enforcement layers - Verifiable agent execution In short: AI agents don’t just need reasoning. They need governance. Curious how others working on AI security and large-scale systems are thinking about this. #MCP #AIAgents #Security #OpenSource #CoFounder #AIInfrastructure #NHI #ModelContextProtocol https://lnkd.in/dHf68TY8