Production-tested patterns for AI agent builders
Data-driven analysis of why most AI agent projects never reach production, with concrete patterns to avoid the common failure modes.
How to integrate x402 stablecoin payments into AI agent services -- the protocol, the implementation, and the economics.
Population-based search for optimal agent strategies. From QuantEvolve's formula discovery to ELfolio's portfolio optimization.
Production-tested strategies to cut AI agent costs by 90% without sacrificing quality. Model routing, prompt caching, context pruning, and more.
15 battle-tested patterns for building reliable multi-step AI agents. From task decomposition to self-healing recovery.
Fault tolerance patterns for AI agents: orchestrator succession, straggler management, graceful degradation, and quality gates.
Build and monetize MCP servers. Directory distribution, usage-based billing, x402 payments, and real economics.
Keep AI agents effective across long workflows. Context preservation, tiered propagation, and when to spawn a fresh agent.
Build AI agents that actually get better over time. The four-phase loop, six loop categories, and meta-evolution.
Extract knowledge from failed agents and strategies. Post-mortem extraction, pattern cataloging, and inoculation.
Anti-monoculture mechanisms for portfolios of AI strategies. Survive regime changes and platform risk.
Budget caps, circuit breakers, and kill switches. Defense-in-depth for agent systems.
Search that gets smarter as it runs. Information scent, satisficing, and diminishing returns gates.