The Self-Improving Stack
Map the parts an agent team can change—prompts, skills, runtime, traces, evaluations, model training, and release rules—and the evidence required before promoting a candidate change.
For teams improving agents, the series helps choose the right layer, protect fresh test cases from the search process, and keep a human release decision at the end.
Start here: The Self-Improving Stack: How AI Agents Get Better. Then continue in the order below.
The Self-Improving Stack: How AI Agents Get Better
The self-improving stack is the set of agent layers that can change, from prompts and skills to runtime, traces, evaluation, model training, and governance.
Prompt Optimization for AI Agents: What It Can Change
Prompt optimization searches instructions, examples, schemas, tool descriptions, and rubrics for better agent behavior while the model and runtime stay fixed.
Skill Optimization for AI Agents: Durable Procedures
Skill optimization improves reusable agent procedures and their activation rules while keeping model weights fixed.
Optimization Theory for Agent Builders
Optimization theory for agent builders helps choose what to change, compare candidates fairly, and stop noisy gains from becoming production regressions.
Post-Training Agents: When to Change the Model
Post-training for agents moves improvement from prompts and skills into model weights or adapters, with harder data, release, and rollback decisions.
Agent Memory: Why Retrieval Is Not Learning
Agent memory becomes learning only when a scoped, evidence-backed write reaches the right future task and evaluation shows better behavior than no memory.
Evaluation Gates: Decide What Agent Changes Ship
Evaluation gates decide whether a change ships by comparing candidate and baseline on protected tasks, deterministic checks, cost, and trace evidence.
Agent Traces: The Evidence an Improving System Needs
Agent traces preserve calls, tool actions, observations, artifacts, costs, and outcomes so the team can diagnose failures, not tune scores alone.
Test-Time Compute for Agents: Beat Random at Equal Cost
Test-time compute adds samples, branches, retries, or checks; an execution shape matters only when it beats a simple baseline at equal cost.
Harness Evolution for AI Agents: When Prompts Plateau
Harness evolution changes the software around an agent when prompts and skills cannot add tools, isolation, checks, traces, or rollback.
Agent Runtime Topology: When Parallel Work Is Real
Agent runtime topology determines whether prompts produce parallel work, reviews, retries, cancellation, and evidence, or only describe them.
Multi-Agent Coordination: Roles Are Not Structure
Multi-agent coordination needs role contracts, state boundaries, budgets, checks, and traces; compare it with a single-agent baseline at equal cost.
Agent Governance: How to Govern Self-Improving Agents
Agent governance sets owners, authority limits, evidence requirements, approvals, and rollback paths before behavior changes reach users.