AI AgentsAI vs AI Agent: What Actually Changes When You Add a Loop
A model answers. An agent decides, acts, and checks its work. The difference is a loop and a set of tools — here is exactly what that means in code.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
Agents, tool calling, and the loop that makes them useful.
AI Agents
AI AgentsBuilding Your First Agent: A Loop, Three Tools, One Guard
Skip the framework. An agent is a while loop over a tool-calling model. Here is the minimum that works, and the four things that break it first.
ReadAI AgentsEvaluating Agent Reliability: Past the Single Pass Rate
An agent that works four times in five is not 80 percent useful. Here is how to measure agent reliability in a way that predicts production behaviour.
ReadAI AgentsHuman in the Loop: Designing Approval That Actually Works
Most approval gates fail not from too little oversight but from too much. Here is how to design agent checkpoints people actually read before clicking yes.
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AI AgentsLong-Running Agents: Durability, Compaction and Restarts
An agent running for hours hits limits a chat never does: context exhaustion, process restarts, stale state. The patterns that keep long jobs alive.
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AI AgentsMCP Explained: One Protocol Instead of N×M Integrations
The Model Context Protocol standardises how agents connect to tools and data. What it solves, how it works, and when a plain function call is enough.
ReadAI AgentsMulti-Agent Systems: When They Help and When They Hurt
Two credible teams published opposite advice on multi-agent architectures within a day of each other. Both were right, because the answer depends on the task.
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AI AgentsPrompt Engineering for Coding Agents Is Mostly Context Engineering
Clever phrasing barely moves the needle. What actually improves agent output is what you put in front of it, in what order, with what constraints.
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AI AgentsPrompt Injection: Why Your Agent Is a Confused Deputy
An agent that reads untrusted content can be instructed by it. The fix is not a better prompt — it is treating the model as an untrusted client.
ReadAI AgentsReAct vs Plan-and-Execute: Two Ways to Run an Agent Loop
ReAct decides one step at a time. Plan-and-execute commits to a plan first. The choice changes cost, latency, recoverability and how failures look.
ReadAI AgentsStructured Outputs: Getting JSON You Can Actually Parse
Asking a model for JSON and hoping is a bug waiting to happen. Schema-enforced outputs, and the validation you still need around them.
ReadAI AgentsSubagents and Delegation: When Splitting an Agent Helps
Subagents buy parallelism and a clean context window, and cost you shared understanding. Here is when that trade is worth making, and when it is not.
ReadShowing 61–72 of 74 articles