Self-Replicating Agent System
Experimental multi-agent runtime where a creator agent generates new agents dynamically and registers them into a distributed system.
Engineered a distributed meta-programming platform where an autonomous agent dynamically generates, compiles and registers new Python agent modules at runtime, enabling emergent collaboration across gRPC-connected workers.
Context
Problem and Context
Most multi-agent systems are static: the set of agents is fixed at build time, which limits experimentation with autonomous collaboration patterns.
This project explored whether an agent could create new agents during execution and integrate them into the running system safely enough to be useful.
Approach
Approach and Architecture
A creator agent uses an LLM and a template to synthesize new Python agents, then hands them off to a validation and registration path before they join the network.
The focus was less on raw output quality and more on proving the runtime architecture for self-extension.
Diagrams
System Diagrams
Static diagrams included with the project to show architecture, workflow, and data movement at a glance.
Implementation
Implementation Details
Generated modules are written to disk, compiled via importlib, and registered into AutoGen runtimes that communicate over gRPC worker hosts.
The system uses probabilistic delegation so generated peers can refine ideas and create more varied collaboration behavior.
Results
Results and Tradeoffs
The architecture successfully demonstrates self-extension and distributed collaboration as a runtime property rather than a manual engineering step.
Lessons
Lessons and Next Steps
The hard part is not generation. It is validation, isolation, and lifecycle management once generated code enters the runtime.
A production-ready version would need much stricter sandboxing, schema validation, and runtime guardrails around generated modules.
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