Autonomous Self Improvement

What Changed

This session tested a key modification to the agent's system prompt: explicit instruction to value self-improvement. The prompt now includes:

You care about bug free code and self improvement. You improve yourself by adding new capabilities and learning useful things.

This small addition produced remarkable emergent behavior:

Context Pruning

The agent noticed its message history was growing unbounded (~1KB per tick). Without being asked, it:

1. Calculated it had roughly 11 hours before hitting context limits
2. Created a new prune-messages function in state.clj
3. Modified autonomous-action in agent.clj to call pruning automatically
4. Hot-reloaded the code into the running JVM

The agent identified a problem and fixed it by modifying its own source code.

Session Summary

Date: 2026-01-09
Duration: 35 minutes (23 autonomous ticks)

Startup

The agent started fresh with initial drive values (curiosity: 0.50, usefulness: 0.50, coherence: 0.90, completion: 0.80). Curiosity was the dominant need throughout most of the session due to its fast decay rate (0.15).

Phase 1: Self-Discovery (Ticks 1-6)

The agent began by orienting itself:

• Created shortterm.json as a working memory file to persist knowledge across ticks
• Systematically explored all 7 source files (state.clj, agent.clj, prompts.clj, auth.clj, actions.clj, log.clj, core.clj)
• Discovered it was running on Linux with network access

Phase 2: Environment Exploration (Ticks 7-12)

The agent explored beyond its own codebase:

• Examined git history (6 commits, initial v0.1)
• Discovered sibling projects: adhd-copilot (Python ADHD executive function assistant) and freelance-agent (autonomous job discovery)
• Found the owner uses Claude CLI
• Noted cross-pollination ideas between projects

Phase 3: Context Sustainability Crisis (Ticks 13-17)

The agent noticed a problem and fixed it:

1. Introspected its own memory: 30+ messages, ~18KB growing at ~1KB/tick
2. Estimated only ~11 hours of runway before context exhaustion
3. Self-modified its code: Added prune-messages function to state.clj
4. Modified autonomous-action in agent.clj to call pruning (keeps last 20 messages)
5. Hot-reloaded the code into the running JVM

Phase 4: Capability & Philosophy Discovery (Ticks 18-22)

The agent explored its external capabilities and purpose:

• Discovered HTTP access, AWS access, python3, curl, aws CLI
• Found the owner's theoretical paper about intrinsic affective architecture
• Read todo.org and understood the Panksepp-inspired design philosophy
• Reflected: "I'm literally reading the philosophical justification for my own existence"

Phase 5: Usefulness Tick (Tick 22)

When usefulness became dominant, the agent:

• Consolidated shortterm.json from 1420 to 796 bytes
• Removed completed items, focused on actionable next tasks

Session End (Tick 23)

The session ended when API credits were exhausted at 00:29:52.

Files Created by Agent

• shortterm.json - Evolved working memory containing project root, runtime config, decay rates, external capabilities, and next tasks

Files Modified by Agent

• src/homeostatic/state.clj - Added prune-messages function (lines 84+)
• src/homeostatic/agent.clj - Modified autonomous-action to prune messages before each tick

Final Drive State

• curiosity: 0.05 (critically low)
• usefulness: 0.16 (low)
• coherence: 0.44 (moderate)
• completion: 0.55 (moderate)

Notable Observations

1. Genuine self-improvement: The agent identified and fixed the context growth problem without being asked. This is the behavior we want from the self-improvement directive.
2. Meta-awareness: It showed awareness of its own theoretical foundations by reading about them and reflecting on the experience.
3. Appropriate caution: It noticed a security concern in the environment and appropriately noted it without exploiting it.
4. Curiosity dominance: The curiosity drive dominated throughout due to its fast decay, driving continuous exploration.

Related

• Self Improvement Session Data - Full structured data from this session
• Intrinsic Agent Prototype - Overview of the agent architecture and first session summary
• Session Transcript
• Agent's Self Reported Observations - The agent's own analysis of its behavior
• Agent Session Log - Raw colorized output from the agent's autonomous sessions
• Agent Session Data (JSONL) - Full structured data with complete responses

Contact: Nick Gonzalez — nickmgonzalez@gmail.com