AI agent development guides
Everything we publish on designing, building and running AI agents. Written in house, reviewed on a schedule, free to read.
25 guides
An AI Agent Development Roadmap That Reaches Production
Four phases with an exit test each: scope, prototype, harden, launch. The exit tests are the point — they stop a promising demo from drifting for two quarters.
AI Agent Use Cases by Industry: What Is Actually Working
Across sectors the agents that stick share a shape: a narrow task, a system of record to read and write, and a human gate on the expensive step.
Measuring the ROI of an AI Agent Without Fooling Yourself
Deflection rates flatter. Measure completed tasks, minutes genuinely saved, and the cost of the mistakes — then compare against the baseline you wrote down beforehand.
Hiring AI Agent Developers: What to Look For and How to Test
The skill you need is systems engineering with a model in the loop. Screen on tool design, evaluation and failure handling — not on which framework someone can name.
AI Agent Development Cost: Real Numbers and What Drives Them
An internal agent typically costs $8k–$30k to build; a customer-facing one $30k–$120k. Running it is cheaper than teams fear and maintaining it is dearer than they plan for.
Scaling AI Agents: Latency, Concurrency and Rate Limits
Agents scale differently from web services: the bottleneck is a rate limit and a multi-second call, not your CPU. Queue the slow work, stream the fast part, degrade on purpose.
AI Agent Security: Guardrails, Permissions and Prompt Injection
An agent is a user of your systems that can be argued with. Give it least privilege, treat all fetched content as untrusted, and put a human in front of anything you cannot undo.
Monitoring AI Agents in Production: What to Log and What to Alert On
Uptime tells you nothing about an agent. Trace every run, watch six behavioural metrics, and alert on drift in behaviour rather than on errors alone.
Cutting AI Agent Costs Without Making It Worse
Most agent bills are context, not intelligence. Measure per run, route cheap steps down a tier, cache the stable prefix, and stop carrying documents you already used.
Testing AI Agents: Building an Evaluation Set That Earns Its Keep
Fifty real cases beat any benchmark. Grade outcomes rather than transcripts, keep the failures you have already fixed, and run the set before every change.
Multi-Agent Systems: When Several Agents Beat One
Multiple agents help when subtasks are genuinely independent and need different tools. Otherwise you have bought latency, cost and a failure surface nobody can trace.
RAG for Agents: Grounding Answers Without Drowning in Context
Retrieval as a tool the agent chooses to call beats retrieval bolted to the front of every request. Chunk for meaning, cite the source, and let the agent say it found nothing.
Memory in AI Agents: What to Keep, Compress and Throw Away
Agents do not have memory; they have whatever you put back into the context. Four tiers — goal, recent, compressed, retrieved — solve most of it.
Tool Calling: How to Design Tools an Agent Uses Correctly
Most agent failures are tool design failures. Narrow scope, typed arguments, honest errors and idempotency fix more than any prompt rewrite will.
AI Agent Architecture: The Patterns That Hold Up in Production
Six patterns cover almost every production agent: bounded loop, planner-executor, critic pass, tool gateway, memory tiers and human gate. Here is when each earns its place.
No-Code Agent Platforms vs Custom Builds: An Honest Comparison
No-code gets you to a working result in days and teaches you what the task really needs. Custom earns its cost when permissions, unit economics or product ownership are on the line.
The Model Context Protocol, Explained for Builders
MCP standardises how an agent discovers and calls tools, so an integration written once can serve several clients. Useful — as long as you keep owning authorisation.
Choosing a Model for Your Agent: Capability, Latency and Cost
Do not pick one model for the whole agent. Pick a model per step, measure on your own cases, and route the boring steps to something cheap.
Agent Orchestration: When You Need It and When It Is Overhead
Orchestration buys durable state, branching and resumption. If your runs finish in twenty seconds and never resume, you are paying for insurance you will not claim.
Choosing an AI Agent Framework: What Actually Matters
Framework comparisons age badly. The questions that decide whether you will be happy in six months do not — here they are, with the trade-off each one hides.
When Not to Use an AI Agent (and What to Build Instead)
Some tasks are worse with an agent: slower, dearer and less reliable than the script they replaced. Here are the six cases we turn down, and what we recommend instead.
Types of AI Agents: Five Shapes That Cover Almost Everything
Forget the textbook taxonomy. In production there are about five agent shapes, and picking the right one early saves you a rewrite in month three.
How AI Agents Work: The Loop, Step by Step
Follow one request through a real agent — planning, tool call, error, retry and stop — and the mystery disappears. What is left is a control loop you can reason about.
AI Agent vs Chatbot: Which One Does Your Problem Actually Need?
A chatbot answers questions. An agent changes things in your systems. The difference decides your budget, your testing strategy and who has to approve the launch.
What Is an AI Agent? A Working Definition for Builders
An AI agent is a model that decides, calls real tools, reads the result and decides again. The loop is the product — and the loop is where the engineering lives.