12 published lessons with this tag.
Objects often move through defined states — tracking these states enables workflow.
Every agent is created, deployed, maintained, and eventually retired.
New agents are most fragile in their first month — they need extra attention and support to survive.
Track versions of your agents so you can compare, rollback, and learn from changes.
Define clear criteria for when an agent should be retired rather than maintained. Without explicit retirement criteria set in advance, you will hold onto agents long past the point where they serve you — because the sunk cost of building them, the identity you attached to them, and the absence of a forcing function all conspire to keep dead agents on life support.
Retire agents gracefully — document what they did, why they're being retired, and what replaces them.
When retiring an agent ensure its responsibilities transfer to a new agent or are consciously dropped.
Understanding your past agents — even failed ones — reveals patterns in how you build cognitive systems.
Some agents outlive their usefulness but persist because removing them feels risky or costly. Legacy agents consume resources, create confusion, and block the deployment of better alternatives. Identifying them is the first step toward a clean epistemic portfolio.
Documentation should evolve with the agent — outdated docs are worse than no docs.
Knowing where each of your agents is in its lifecycle helps you allocate attention appropriately.
The way you create, maintain, and retire agents mirrors how you learn, practice, and let go of knowledge. Recognizing this parallel turns agent management into a form of self-directed development.