the-self-improving-stack
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: One Layer of a Bigger System
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: Training 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 is a way to choose the right mutable surface, compare candidates fairly, and keep noisy improvements from becoming production regressions.
Post-Training Agents: When to Change the Model
For teams building post-training agents, changing model weights or adapters moves improvement from prompts and skills into the policy that acts across future contexts.
Agent Memory: Why Retrieval Is Not Learning
Agent memory becomes a learning loop only when a past run creates a scoped, supported write, the right future task retrieves it, and evaluation proves that behavior improved.
Evaluation Gates: The Rule That Decides Whether an Agent Improves
Evaluation gates turn agent evaluations into release decisions by comparing a candidate with a baseline on protected tasks, deterministic checks, cost limits, and trace evidence.
Agent Traces: The Evidence an Improving System Needs
Agent traces preserve model calls, tool actions, observations, artifacts, costs, and outcomes so an improvement loop can diagnose a failure instead of tuning a score alone.
Test-Time Compute for Agents: Beat Random at Equal Cost
Test-time compute gives an agent extra samples, branches, retries, or verification, but an execution-shape claim matters only after it beats a simple equal-budget baseline.
Harness Evolution for AI Agents: When Prompts Plateau
Harness evolution changes the execution software around an agent when prompt and skill tuning cannot create the missing tools, isolation, checks, traces, or candidate lifecycle.
Agent Runtime Topology: How Execution Shape Changes Agent Behavior
Agent runtime topology determines whether a request for parallel work, review, retries, cancellation, and evidence becomes an executed workflow or remains a sentence in a prompt.
Multi-Agent Coordination: Roles Are Not Structure
Multi-agent coordination becomes real when roles have contracts, permissions, state boundaries, budgets, checks, and traces, and when the system beats a single-agent baseline at equal compute.
Agent Governance: How to Govern Self-Improving Agents
Agent governance gives self-improving systems owners, authority limits, evidence requirements, approval points, and rollback paths before mutable behavior reaches users.