18 posts tagged with "Agents"

Enterprise AI solution engineering is not the practice of connecting a user interface to a large language model and calling the result production-ready. It is the discipline of converting a real business problem into an AI-enabled operating capability that is valuable, secure, reliable, measurable, governable and sustainable.

That requires much more than model knowledge. An AI solution engineer must work across business strategy, user experience, process design, data and knowledge architecture, AI engineering, cloud platforms, integration, cybersecurity, privacy, responsible AI, software delivery, evaluation, operations, FinOps and organizational change.

The central question is therefore not:

Which model should we use?

It is:

What business capability are we improving, what evidence will demonstrate success, what is the safest and simplest architecture that can deliver it, and how will the organization operate it responsibly at scale?

This article provides a complete reference for answering that question.

Version note: This article reflects public guidance available on 21 August 2026. Laws, standards, provider services and AI capabilities evolve quickly. Regulatory interpretations should be confirmed with qualified legal, privacy, risk and compliance specialists for the relevant jurisdiction and use case.

Agentic coding is not autocomplete with a better model. A coding agent can inspect a repository, run commands, edit multiple files, execute tests, and continue until it believes the task is complete. That autonomy is valuable, but it changes the engineering problem.

The central challenge is no longer only “How do I write a good prompt?” It is:

How do I design a development environment in which an agent receives the right context, operates within safe boundaries, verifies its work, and leaves behind maintainable decisions?

Using Claude only as a question-and-answer chatbot is like buying a powerful computer and using it only as a calculator. The real value appears when you organise Claude around your actual workflow — and give each part of that workflow a proper home instead of one giant prompt.

Context is the scarce resource in every coding agent session. Instructions, file reads, tool schemas, MCP servers, and your chat history all compete for the same window — and quality degrades before the hard limit. Part II covers how Cursor and Claude Code expect you to structure rules, skills, and session hygiene.

One disciplined agent is leverage. Ten undisciplined agents are a tax. Part IV covers parallel execution, cloud delegation, independent review, and the failure patterns that turn agentic coding into expensive thrash.

Ask-and-pray prompts turn frontier models into expensive autocomplete. The teams that ship consistently do the opposite: they force research and a reviewable plan before any file write. This article is Part I of a four-part series on working with coding agents in production.

Without a check the agent can run, “looks done” is the only stop signal — and you become the verification loop. Part III covers how Cursor and Claude Code turn tests, builds, screenshots, and hooks into an oracle that stays honest even when context degrades.

Artificial intelligence leadership is often misunderstood as the ability to select the best model, approve an AI strategy or sponsor a portfolio of proofs of concept. Those activities matter, but they are not the essence of leadership.

AI leadership is the disciplined conversion of uncertain technological capability into measurable, secure, governed and socially acceptable outcomes.

Claude Code is easy to misread as "chat that writes files." It isn't. It is an AI coding agent with an agentic loop, a context window, tools, and a permission system — you steer it through Explore → Plan → Code → Commit, then harden the workflow with CLAUDE.md, skills, subagents, MCP, and hooks.

Artificial intelligence models are improving at a remarkable pace. A model released only six months ago can quickly appear less capable, less efficient and less reliable than a newer generation. It is tempting to assume that every new model is smarter because it contains more parameters — but modern frontier systems such as Claude Fable 5, Claude Opus 5, GPT-5.6, Gemini 3.6 Flash and Kimi K3 are improving through a much broader stack of advances.