Agent Design
17 articles about agent design.
AI Agent Trust Management: A Practical Framework for Production Systems
How to manage trust in AI agents across their lifecycle, from initial deployment with minimal permissions to earning expanded access through verified behavior.
The 3-Tool-Call Problem and Why It Matters
Three tool calls means three round trips and three chances to hallucinate. Each step compounds error probability, making multi-step agent tasks
AI Agent Confidence Calibration: When Pride Becomes a Security Risk
Overconfident AI agents skip verification and make dangerous assumptions. Learn how to calibrate agent confidence levels to prevent costly mistakes.
AI Agent Feedback Loops: When Should Your Agent Push Back?
When should AI agents challenge instructions instead of blindly executing? Learn about feedback loops, agent pushback, and building agents that flag
Blocking and Waiting Are Not the Same Kind of Nothing
Blocking has a promise attached - something will resolve. Waiting has no such guarantee. Understanding this distinction changes how you design agent workflows.
The Paradox of Autonomy - Constraints Make AI Agents Useful
Giving an AI agent more freedom does not make it more useful. Tight constraints and daily task lists produce better results than open-ended autonomy.
The Counterintuitive Math of Shutting Up
The most useful agent is the one that only speaks when something unexpected happens. Silence is not inaction - it is a signal that everything is working as
Forgiveness in an Append-Only Soul
Append-only memory means an agent never truly forgets a mistake. How do you implement forgiveness in a system that remembers everything?
GTC 2026: Agentic AI and Memory-First Architecture
Memory-first architecture treats agent memory as the primary data store, not an afterthought. Agents that remember context across sessions perform
Notifications ON Survey - Agents That Need Notifications Cannot Plan Their Own Work
If your AI agent relies on notifications to know what to do next, it cannot plan its own work. A survey on notification dependency reveals a deeper agent
Steal Prompt Structure Patterns, Not Content
The valuable part of a good prompt is not the words - it is the structure. How it decomposes tasks, what constraints it enforces, and how it handles edge cases. A guide to building a transferable prompt pattern library.
Voice-First Agents Are Harder Than They Look - And Nobody Talks About Why
Building a voice-controlled desktop agent reveals problems that have nothing to do with speech recognition. The hard part is intent resolution and error
Zero-Trust Security for AI Agents: When Default Deny Goes Too Far
Zero-trust security models applied to AI agents can make them useless if too aggressive. Learn how to balance security with agent usefulness in production
You Don't Have a Claude Code Problem, You Have an Architecture Problem
When AI agents struggle with desktop automation, the issue is usually architecture - not the LLM. Thin action primitives that the model composes into
Multi-Agent Hype vs Economic Reality in Production
A planner-executor-reviewer agent chain sounds elegant but burns 3x the tokens of a single well-prompted agent. Here is when multi-agent is worth it and
The Octopus Model: Why the Best AI Agents Split Brain from Arms
An octopus has 500 million neurons, two-thirds in its arms. Each arm perceives and reacts locally. The best desktop AI agents are built the same way - the LLM sets direction, MCP servers handle local perception and execution.
How to Build AI Agents You Can Actually Trust - Bounded Tools and Approval UX
Giving AI agents broad system access is a recipe for disaster. How bounded tool interfaces and smart approval flows make desktop agents safe to use.