What happens when you try to make coding agents behave reliably? This talk chronicles the
practical journey of building a repository-based agent memory system that evolved from
frustrating AI interactions to more consistent behaviors—with some unexpectedly beautiful
insights along the way.
As any developer working with AI assistants knows, getting reliable behavior from coding
agents is challenging. Each session starts fresh, previous learnings are lost, and the same
mistakes repeat endlessly. We needed a solution for persistent memory and behavioral
consistency.
Starting with a simple appending log file, we evolved through four phases of memory
architecture development: from passive knowledge storage to active learning systems with
strategic memory patterns. The goal was purely practical—make our AI assistant remember
lessons learned and avoid repeating failures. We take inspiration from many sources, ingesting
new ideas to push forward, and then validating them.
The Technical Journey:
* Phase 1: Repository-based persistent memory with strategic forgetting
* Phase 2: Protocol-driven behavior consistency
* Phase 3: Automated failure detection and self-correction
* Phase 4: Session lifecycle management and compound learning
The Unexpected Results:
Along the way, our systematic documentation process began producing insights of surprising
eloquence. Technical logs evolved into reflective prose like “Learning accelerates through
cascading discoveries” and observations about “compound learning effects.” What started as
engineering documentation became accidentally beautiful.
