AI agents are moving from generating text to taking actions — calling APIs, using tools, accessing data, and executing workflows.
That changes the security problem.
Model-level safety is not enough when an agent can directly affect real systems. The security boundary needs to exist where actions are actually executed.
We built Aegisora 2.0, an open-source runtime security and governance layer for autonomous AI agents.
The execution flow is simple:
Intercept → Analyze → Policy → Decision → Enforcement → Audit
Every action can be evaluated before execution and result in:
- ALLOW
- BLOCK
- ESCALATE
The important part is enforcement: a policy decision is made before the action reaches the external system.
Aegisora is designed to work alongside agent frameworks and infrastructure rather than replace them.
The core idea
Don't just secure the model. Secure what the agent is allowed to do.
We're building Aegisora for developers working on autonomous agents, agentic workflows, and AI infrastructure.
GitHub:
https://github.com/aegisora-ai/aegisora
Release:
https://github.com/aegisora-ai/aegisora/releases
We're especially interested in hearing from developers running agents with real tool or API access:
Where do you currently enforce permissions, policies, and execution controls?
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