Agron — Self-Improving Multi-Agent AI

Agron is an autonomous, self-improving AI agent system that learns from scratch. It starts with empty memory and bootstrap rules, then recursively improves through:

1. Observation of task outcomes
2. Memory consolidation and knowledge graph growth
3. Tool/strategy evolution and validation
4. Safe self-modification with tests
5. Optional synchronization to Hugging Face

Agent Team

Agent	Role	Specialty
Coordinator	Orchestrator	Decomposes work, delegates, synthesizes
Learner	Knowledge	Ingests, distills, updates memory from scratch
Builder	Improvement	Generates new tools/strategies recursively
Guardian	Safety	Validates changes, prevents regressions

Learn From Scratch

Agron begins with:

• Empty episodic memory
• No imported knowledge
• Seed evaluation rules only
• Bootstrap loop enabled

First cycles prioritize:

• Environment probing
• Capability discovery
• Minimal safe tool creation
• Curriculum expansion
• Test-backed improvement

Recursive Self-Improvement

• XP / generation tracking
• Gap analysis against current capabilities
• Candidate generation with validation
• Test-gated commits
• Misalignment/secret checks before push
• Rollback on failed validation

Hugging Face Sync

Agron can push validated improvements to: https://huggingface.co/teddyoas/Agron

Trained LLM

Agron's initial LLM has now been trained from scratch on WikiText-2 using a 4-layer, 256-dim, 4-head LLaMA-style architecture (about 8.4M parameters). Training artifacts and weights are committed in this repository, including model.safetensors, tokenizer files, and the checkpoint.

Future recursive self-improvement can fine-tune or grow this base model.

Cybersecurity Focus

Agron and its spawned agents are constrained to cybersecurity exploitation and analysis only:

• Exploitation (authorized targets, CVEs, payloads)
• Vulnerability analysis and research
• Reverse engineering and malware analysis
• Network / host / application reconnaissance
• Defensive hardening and detection engineering

Agents can be spawned in parallel for tasks using the built-in multi-agent orchestration.

LLM + Agent Spawning

Agron is an LLM at its core, not only an agent system. Its model weights are trained and published in this repository, and the runtime can use the model to power (or seed) specialized cybersecurity agents that are spawned in parallel for tasks:

• ExploitAgent
• AnalysisAgent
• ReconAgent
• VulnAgent
• DefenseAgent

Agents can be created on demand for task decomposition, then run concurrently, while the backbone remains the Agron LLM.