Building with AI agents — development patterns, automation design, and what works in production.

Ten AI agent builders compared on how you build an agent, who builds it, how it acts on business systems, and what it costs, with each one's best fit.

Paul Turner

Eleven MCP gateways compared on what they govern, how they handle identity and audit, where they run, and what they cost, with each one's best fit.

Paul Turner

Start with one measurable workflow, connect the systems before you add the agent, give it narrow tools, and gate every write. Four ways to build it, compared.

Paul Turner

Eight platforms that build AI agents and connect them to your systems, compared on how agents and integration fit together, what each costs, and where each one fits.

Paul Turner

Eight controls for running AI agents safely at enterprise scale: inventory, identity, approvals, audit trails, customer data, untrusted input, release gates and an off switch.

Paul Turner

MCP is how AI agents find and call tools; an API is how a system exposes its functions. Why enterprise MCP tools work better built on APIs than stacked behind a proxy.

Paul Turner

The hard part of an AI agent is what happens when it acts. What to look for in a builder, where each type fits, and how to test one on your own systems.

Rich Waldron

The pilot proves the model can answer. Production asks what it can change, as whom, and who picks up the rest. Where agent deployments stall, and what to fix.

Paul Turner

Access to MCP tools only grows. Why access reviews change nothing, and how to run one that owners act on.

Adam White

Most teams govern MCP after the fact. The choice between proxying the servers you have and replacing them decides most of what follows.

Alistair Russell

Approval for everything trains people to click yes. When an agent should ask first, when it should hand off, and what the person needs to see.

Rich Waldron

The six AI automation patterns, from fixed workflows to autonomous agents, and how to pick the right one for each problem.

Stephen Stouffer

A tool changes its inputs on a Friday and every agent that used it fails. Why MCP servers need owners, versions and retirement dates.

Alistair Russell

One line changes in the agent's instructions, and it stops escalating. Why AI agents pass the demo and fail the week after, and how to test before each release.

Alistair Russell

Your AI bill climbed and no single team is to blame. The answer is not in your query logs. It is in the tool definitions loading before anyone types.

Adam White

Inconsistent agent behavior is a tool design problem, not a model problem. How J.W. Pepper made MCP agents reliable with workflow-backed tools.

Adam White

Every office has a propped-open side door nobody watches. An ungoverned MCP server is that door, and it inherits whatever access its builder had.

Adam White

A study of a thousand MCP servers found a third carrying critical vulnerabilities. Gartner expects incidents in 40% of enterprise deployments by 2027.

Paul Turner

A practical comparison of ChatGPT, Claude, and Gemini for enterprise AI agents, focused on performance, security, governance, and real-world operability.

Adam White

Agents, copilots, and chatbots serve different roles in enterprise AI. Learn how they differ in autonomy, capability, and architecture, and when to use each.

Adam White

Learn how enterprises choose, integrate, and govern LLMs. A practical guide to model selection, RAG, orchestration, and building AI systems that scale.

Alistair Russell

The Model Context Protocol (MCP) accelerates AI agent building, but without governance, it risks data and control. See how to secure it for enterprise use.

Alistair Russell

HR holds the most sensitive data in the company and the most repetitive queue. That combination is why it is a good first place to put an AI agent.

Adam White

Not every workflow needs an AI agent. Learn when to use AI agents and how to find the enterprise “sweet spot” for automation.

Adam White

Should you reach for a developer framework or a unified platform? There is no universal best answer. The right one depends on your use case and your team.

Michael Douglas

AI readiness requires more than models. Learn how Yext transformed integration into AI infrastructure to scale AI safely across the enterprise.

Adam White

MCP standardized the interface, not the security or governance of what runs on it. What the protocol does well, and what production deployment still needs.

Alistair Russell

58% of organizations say IT loses five-plus hours a week to repetitive requests. What an ITSM agent solves, and how to deploy one without the pitfalls.

Adam White

Every access request you approve by hand is time IT does not get back. A support agent that provisions apps and checks entitlements, built in minutes.

Luke Smith

The experimental phase of generative AI is over. Getting from prototype to production is an integration problem long before it is a model problem.

Paul Turner

A knowledge agent answers from your own documents instead of guessing. How to build the RAG pipeline behind one, and where the security work goes.

Tom Walne

Explore the AI Adoption Ladder: a guide to transitioning from AI curiosity to strategic integration, unlocking transformative growth and competitive edge.

Paul Turner

Learn how sales teams can best leverage AI to revolutionize their sales strategy in this guest blog from HubSpot.

Tony Do

If you are cutting corners on a process because the tool cannot draw it, the tool is the problem. When point-to-point automation stops being enough.

Adam White