Agile Buddha

A team I worked with got access to AI coding tools. Good engineers, real delivery pressure. Within a few weeks, they were writing code noticeably faster.

Then they looked at their sprint results.

Nothing had changed.

The same number of features were reaching users. The same delays at testing. The same wait for approvals. AI made the coding faster. Everything around it stayed exactly the same.

This is the story of that gap – between AI freeing up time and your business actually getting something from it. It is a gap most organisations are not measuring. And it is where most AI transformation programmes quietly fail.

The freed time goes somewhere — just not where you think

When AI helps a developer write a feature in two hours instead of four, those two hours are real. They have been freed. That is not nothing.

But where do those two hours actually go?

[Read more…] about AI Freed the Capacity. Then What?

The question is not whether to use agents. It is which actions in the agent’s task list cross the threshold that requires a human gate.

In 2026, Palo Alto Networks described a case that should worry anyone deploying AI agents. A marketing team built an agent to read Salesforce records and generate leads — a legitimate, even boring, use case. 

The agent also had permission to delete records. Nobody had asked what permissions the agent actually needed. That is not carelessness. Low-code deployment tools are designed to remove friction. 

Nothing about the agent was malicious. Nothing about the team was careless by ordinary standards. The question that would have caught it simply never came up.

Something shifted in the last twelve months. 

Instead of asking ‘Should we use AI in enterprise?’, the question has moved to ‘How do we go agentic?’. 

Vendors are pitching AI-native architectures. Jeff Sutherland is talking about autonomous delivery systems.

The excitement is understandable. The capability has genuinely increased. But underneath most of the agentic conversation, a critical question is going unasked — and in regulated environments, that omission is a governance failure waiting to happen.

The question is not: should we use agents?

The question is: for which specific actions is autonomous execution appropriate, and what is the threshold that requires a human gate?

The organisations doing agentic well are making this calculation action by action. The ones doing it badly are deploying agents and discovering the boundary conditions the HARD way.

[Read more…] about Enterprise AI Agents Need Clear Governance Boundaries, Not Blind Autonomy

Most conversations about AI in software delivery start with the tool – e.g. context framework, Cursor, which model to use, which vendor.

They skip the harder question: is your delivery pipeline ready for AI in the first place?

I have been working with banks and large enterprises on delivery transformation over the years.

I recently built an AI-augmented delivery pipeline from scratch on actual code — five stages with real GitHub integration, security scanning, and compliance narrative drafting.

Here is what I found: AI applied to a well-functioning pipeline reduces friction. AI applied to a broken pipeline just moves the constraint forward. 

The speed of the wrong thing is not progress.

Before you add AI to your pipeline, here are seven things that need to be in place or actively in progress.

[Read more…] about 7 Things A Delivery Pipeline Needs Before AI Can Help

There is no shortage of articles telling you what RAG is.

This is not one of them.

This is what happens when you actually build one — over real content, with real questions, and an honest evaluation of what works and what does not.

What Is RAG and Why Should You Care?

RAG stands for Retrieval Augmented Generation.

The idea is simple. Before answering your question, the AI searches your documents, finds the most relevant pieces, and uses those to construct a grounded answer — not a generic one.

Think of it as the difference between asking a colleague who has read your internal policy documents versus asking someone who has only read the internet.

Most enterprise AI assistants being deployed right now use this approach. Internal knowledge bots, policy assistants, code documentation tools — RAG is underneath most of them.

What I Built

I wanted to understand what enterprises actually face when they deploy these systems. The only honest way to do that was to build one myself and stress-test it.

So I built a RAG system over 14 years of my own content — 111 blog posts from agilebuddha.in and my book on Agile estimation. Then I ran real questions against it and documented every failure.

The technology itself took eight focused sessions of 60-90 minutes each. It is not rocket science.

What came after building it — that is where it gets interesting.

What I Found

[Read more…] about 14 Years of Writing. One AI System. Here’s What It Found.

I was recently working with a software team that just received AI coding tool licences, one for every developer on the team. Their leadership was excited. The CTO had made the call personally. AI-assisted development was going to change everything.

Sprint 1 ended. Not a single story was done.

Not in review. Not in testing. Not deployed. Done.

I want to tell you this had nothing to do with AI coding tool.

The team did not know how to use it well yet – that was true – but that was not the reason.

The real reason was that the constraint was somewhere else entirely.

[Read more…] about Your Team Got AI Tools. Why Isn’t Anything Moving?