Agent Design
How you actually build AI agents that work. Architectures, tool use, memory patterns, and the frameworks worth paying attention to.
Deep Dives and Frameworks
Implementation playbooks, operator patterns, and durable analysis.
Signals, Maps, and Watch Lists
Production-oriented analysis, benchmarks, and market/system intelligence.
External tools
Execution tooling is separate
Swarm Signal keeps the analysis layer. Use BoredTools for reusable production templates and trackers.
Agent State Migration and Rollback: The Missing Reliability Layer
Agent state migration rollback is becoming the reliability layer between agent memory, workflow versioning, and production recovery.
Self-Improving Agents Have an Evaluator Problem
Anthropic's June 2026 update on recursive self-improvement is not a distant sci-fi warning. The company says its engineers now ship 8x as much code per...
The 12-to-72 Problem: Computer-Use Agents Hit Human Scores but Miss the Point
Computer-use agents jumped from 12% to 72% on OSWorld in 18 months. The scores look like progress. The latency and efficiency numbers tell a different story.
Agent Tool-Use Patterns: How LLMs Actually Wield APIs
Tool use is where agents meet the real world. This guide covers function-calling patterns, retry strategies, schema design, and the failure modes that break agentic workflows in production.
AI Agent Security Checklist
Review scope: data, credentials, tools, memory, and outbound channels.
The Agent Project That Should Have Been One LLM Call
Some enterprise agent projects fail because autonomy was added where a bounded single-call LLM design would have delivered cleaner behavior and lower operational risk.
Why Multi-Agent Papers Don't Replicate in Production
A paper from Tran and Kiela tested 28 multi-agent configurations across four architectures: Sequential, Parallel, Debate, and Ensemble. Every single one...
Types of AI Agents: The 2026 Classification That Actually Helps
The reactive/deliberative/hybrid taxonomy is broken. The 2026 classification that actually helps: coding agents, research agents, computer-use agents, task agents, multi-agent orchestrators, and self-improving agents.
Multimodal Agents Score 40% Where Humans Score 72%
Every frontier lab now ships models that see, hear, and read. The assumption is that more modalities mean more capable agents. The benchmarks tell a...
AI Coding Agents: What Actually Works in Production
GitHub reports that 46% of all new code is now AI-generated. Ninety-two percent of US developers use AI coding tools daily. Claude Code hit $2.5 billion...