A career across the full breadth of IT.

Autonomous agents are missing one important analytics feature: the analyst’s raised eyebrow.

A human notices when a price looks stale or a revenue number feels suspicious. An agent can follow the workflow perfectly and act confidently on the wrong value. More intelligence does not repair missing context.

Before expanding autonomy, teams need to make human sanity checks explicit: freshness rules, data contracts, quarantines, agreed metric definitions and traceability. Start with read access, then earn the right to write.

The encouraging part is that this work is practical and incremental. It also improves the systems humans already use.

Which important data check in your organization still exists only inside someone’s head?

Source: Making Your Data Ready for Agentic AI

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AI is making code abundant. Good software remains inconveniently scarce.

Generating a change faster does not help much if environment setup, CI, review and verification stay slow. A thirty-minute pipeline can defeat every frontier model you point at it, without displaying the slightest intelligence.

The useful metric is not lines of code or agent-written pull requests. It is cost per accepted change: the full journey from intent through validation to something we are willing to run in production.

Coding agents do not remove engineering constraints. They expose them faster—and move human attention toward judgment, architecture and evidence.

Where is the real bottleneck after code generation?

Source: When code is abundant

#CTOThoughts #AgenticEngineering

Hiring an entry level developer in 2026: “Must have five years of experience reviewing code generated in 30s”.

AI coding has created in interesting paradox.

The people getting the most value from coding agents are usually the experienced engineers. They know what good looks like so they can spot suspicious output and recognise when the AI has pulled an API endpoint out of the hat.

So, how do we get junior developers to acquire that judgment if AI removes the struggle that traditionally created it?

One way to do it is to preserve some productive friction:

• Explain the approach before generating the code
• Own and defend any generated code
• Actually spend some time to debug problems instead of going to the agent right away
• Measure understanding, not lines of code produced

We need to be better at enhancing the learning experience with AI, not replacing it.

If every junior developer becomes an AI manager on day one … who becomes the senior engineer five years from now?

#CTOThoughts #AugmentedEngineering

Inspired by the article AI Coding will Prevent Expertise