Agent Design
How you actually build AI agents that work. Architectures, tool use, memory patterns, and the frameworks worth paying attention to.
Guides and explainers
Detailed guides and practical technical analysis.
Latest analysis
Recent research, benchmark reviews and technical updates.
Practical tools
Templates for budgets and project planning
BoredTools offers practical spreadsheets for budgets, freelance work and small projects.
Agent Tool Menus Are a Safety Surface
New agent benchmarks suggest the visible tool menu is not a neutral implementation detail. It changes success, cost, wrong-tool calls, and risk exposure.
Agent Benchmarking Doesn't Need Every Task
Efficient agent benchmarking points to a cheaper way to compare agents: run the tasks that still separate systems, not every task in the suite.
Tool Agents Need State Diffs, Not API Call Scores
Tool-use agents are moving from choosing the right API to changing live product state. That makes a clean function-call score too small a test because...
Self-Improving Agents Need Hard Boundaries
Self-improving agents can rewrite code, prompts and memory. Production teams need rollback, approval gates and evaluator change control.
Agent Benchmarks Need Runtime Receipts, Not Model Labels
RuBench's revised 19 July 2026 release contains a small but important warning for coding-agent buyers: one audited product configuration silently...
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.
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.
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...