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How to ThinkIn the Age of AI

Lessons tagged “lifecycle”

12 published lessons with this tag.

schema

Status types track lifecycle

Objects often move through defined states — tracking these states enables workflow.

agents

Agents have a lifecycle from creation to retirement

Every agent is created, deployed, maintained, and eventually retired.

agents

The first 30 days are critical

New agents are most fragile in their first month — they need extra attention and support to survive.

agents

Agent versioning

Track versions of your agents so you can compare, rollback, and learn from changes.

agents

Agent retirement criteria

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.

agents

Clean agent retirement

Retire agents gracefully — document what they did, why they're being retired, and what replaces them.

agents

Agent succession

When retiring an agent ensure its responsibilities transfer to a new agent or are consciously dropped.

agents

Agent archaeology

Understanding your past agents — even failed ones — reveals patterns in how you build cognitive systems.

agents

Legacy agents

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.

agents

Agent documentation lifecycle

Documentation should evolve with the agent — outdated docs are worse than no docs.

agents

Agent lifecycle awareness

Knowing where each of your agents is in its lifecycle helps you allocate attention appropriately.

agents

The agent lifecycle mirrors the learning lifecycle

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.

agents — shares 11 lessons · 187 totalagentsmaintenance — shares 3 lessons · 19 totalmaintenanceretirement — shares 3 lessons · 3 totalretirementdocumentation — shares 2 lessons · 15 totaldocumentationknowledge-transfer — shares 2 lessons · 5 totalknowledge-transfermetacognition — shares 2 lessons · 78 totalmetacognitionattention — shares 1 lesson · 70 totalattentionawareness — shares 1 lesson · 3 totalawarenesscognitive-load — shares 1 lesson · 64 totalcognitive-loadcognitive-systems — shares 1 lesson · 5 totalcognitive-systemsdeployment — shares 1 lesson · 2 totaldeploymentgraceful-degradation — shares 1 lesson · 2 totalgraceful-degradationkill-criteria — shares 1 lesson · 2 totalkill-criterialearning — shares 1 lesson · 24 totallearningmonitoring — shares 1 lesson · 25 totalmonitoringhabits — shares 1 lesson · 91 totalhabitspatterns — shares 1 lesson · 49 totalpatternschangelog — shares 1 lesson · 2 totalchangelogmental-models — shares 1 lesson · 28 totalmental-modelssunk-cost — shares 1 lesson · 3 totalsunk-costsystems-thinking — shares 1 lesson · 75 totalsystems-thinkingphase-capstone — shares 1 lesson · 11 totalphase-capstonesection-capstone — shares 1 lesson · 4 totalsection-capstonesynthesis — shares 1 lesson · 30 totalsynthesisworkflow — shares 1 lesson · 14 totalworkflowretrospective — shares 1 lesson · 3 totalretrospectiveversioning — shares 1 lesson · 3 totalversioningschema — shares 1 lesson · 199 totalschematechnical-debt — shares 1 lesson · 4 totaltechnical-debtversion-control — shares 1 lesson · 2 totalversion-controllifecycle12 lessons