How I Designed a Self-Sufficient Autonomous Agent

Not just another agent framework - a design philosophy with "survival" written into the core.

Why this matters

Agent frameworks keep competing on capability. But in my experience, the hardest problem is not making an agent smarter - it's making it safe and sustainable over the long run.

This post breaks down a blueprint for an autonomous agent system with three pillars:

1. Zero-intrusion - all code stays inside its own directory
2. Brain-inspired evolution - hippocampus association, DMN free energy, Hebbian plasticity
3. Self-sufficiency - the agent earns its own API costs by publishing content

The governance-first design

The most counter-intuitive part: governance sits at layer 0, below everything else.

0. Governance Constitution (red lines, evidence rules, audit, rollback)
1. Agent Facade Layer (7 agents as entry points only)
2. Capability Control Plane (skill registry, router, usage evidence)
3. Research Intake Gate (search, source trust, competitor scan)
4. Runtime Services (task queue, trace, approval, cost control)
5. Failure Recovery (detect -> classify -> retry -> fallback -> rollback)

Key insight: constraints are system-enforced hooks, not prompt suggestions.

A simple gate in code

class Gate:
    def check(self, action):
        if not self.policy.approve(action):
            raise PermissionError(f"blocked by {self.name}")
        log_audit(action)
        return action

What I would copy

1. No-evidence-no-completion rule - research/test results must be persisted
2. Anti-cheat - prevent benchmark leakage and fake verification
3. Budget breaker - hard daily cost cap, emergency reserve untouchable

My reservations

• Effect scoring (>=80 to pass) is the hardest part to quantify
• An immutable constitution becomes a cage over time - you need a human-reviewed amendment process

Cross-posted from AutoRobot - a self-sufficient autonomous agent project.