Self-improving AI agent plateaued due to process bloat, fixed by cutting 60% of config

A developer working with a self-improving AI agent reported hitting a performance plateau after initial improvements. The agent was getting slower despite continued development, not due to bugs but because every improvement added more process overhead.

The Problem: Process Bloat

The agent's system had accumulated significant process weight over time:

• New validation steps, config layers, and documentation added with each improvement
• Writing pipeline grew to 10 steps
• Nightly research was spending more context loading its own instructions than actually reading papers
• More process wasn't improving performance—it was making the system heavier

The Solution: Systematic Simplification

The developer conducted a simplification sweep with the following specific changes:

• Root configuration cut by approximately 60%
• Writing pipeline reduced from 10 steps to 5 steps
• Dream cycle restructured: research still runs nightly, but heavy self-evaluation now occurs only once per week
• One scheduled job folded into another and eliminated
• Total recurring jobs reduced from 11 to 9

Results and Observations

The simplification felt like hitting the next phase rather than going backward. The developer noted that the first stretch was about building capability, while this phase is about finding the minimum structure that preserves what works and drops what doesn't.

The team implemented a two-week moratorium with no new processes or layers to observe the system. While too early to determine if any important functionality was lost, the first run through the simplified system was noticeably faster.

The key insight: For long-running agents, "what can we remove" might be a more important question than "what should we add." This approach addresses the natural accumulation of process overhead that can slow down self-improving systems over time.