Agent Design
How you actually build AI agents that work. Architectures, tool use, memory patterns, and the frameworks worth paying attention to.
Key Guides
Latest Signals
- Anthropic's 186-Deal Experiment Shows What the Agent Economy Actually Looks Like
- When NOT to Use an Agent: The Production Data That Should Change Your Default
- Why Multi-Agent Papers Don't Replicate in Production
- Multimodal Agents Score 40% Where Humans Score 72%
- 2026 Is the Year of the Agent. Here's What the Data Actually Says
From the team behind Swarm Signal
Track Your Finances While You Build AI
BoredTools makes the boring stuff easy — budget dashboards, freelance trackers, and business planners. Download free or grab the full collection.
Most Agent Benchmarks Test the Wrong Thing
The SciAgentGym team ran 1,780 domain-specific scientific tools through current agent frameworks. Success rate on multi-step tool orchestration: 23%. Same...
When Multi-Agent Systems Break: The Coordination Tax Nobody Warns You About
LLM-powered multi-agent systems fail at coordination 40-60% of the time in production environments, according to new research from teams building...
Types of AI Agents: Reactive, Deliberative, Hybrid, and What Comes Next
SWE-bench accuracy went from 1.96% in 2023 to 69.1% in 2025. Understanding the types of AI agents behind this progress (reactive, deliberative, hybrid, and autonomous) is the difference between building tools that work and tools that impress.
Your AI Agent Can Reason, Plan, and Code. It Still Can't See the Web.
AI agents can reason, plan, and code. But they still can't reliably see the live web. The observation layer is the real bottleneck for production agents.
How to Test and Debug AI Agents
Agents that call APIs, write to databases, and send emails can't be tested like chatbots. A complete guide to failure taxonomies, debugging tools, and evaluation pipelines.
The Protocol Wars Nobody's Winning
Ten competing agent protocols and counting. MCP won the tool layer but shipped without authentication. The alphabet soup is a coordination failure.
Agents That Rewrite Themselves: The Self-Modifying Stack Is Here
Three independent papers demonstrate agents rewriting their own training code, generating their own knowledge structures, and refining their reasoning at test time. Self-improvement has moved from theory to working engineering.
Tools That Think Back: When AI Agents Learn to Build Their Own Interfaces
The first generation of agents treated tools as static functions. The emerging generation reasons about tools, remembers usage patterns, and adapts to heterogeneous interfaces.
From Prompt to Partner: A Practical Guide to Building Your First AI Agent
Agents have moved from academic benchmarks to production systems processing millions of conversations. The gap between hype and reality comes down to architecture. This guide walks through model selection, tool design, and instruction engineering with production examples.