16 June 2026 · Mike Hyland

Welcome to the Agentic Era. Ready or Not

The direction is obvious. The destination is far less certain.

There's a strange feeling hanging over the AI industry right now. Not the buzz of triumph from keynote stages or the giddy rush of billion-dollar valuations. No, this one feels like developers getting sucker punched by the liability of what they're shipping, CTOs staring at bills they can't predict and CEOs selling a future they can't quite price. GPU clusters are running at full tilt, engineering teams tokenmaxxing every thinking step while usage dashboards climb with every refresh and finance departments quietly hyperventilating into paper bags.

Welcome to the agentic era.

The conversation has shifted. We are no longer talking about chatbots that answer questions but about systems that work while you're asleep. OpenAI's acquisition of Ona and its push toward persistent Codex environments signals a future where agents operate continuously across software projects and enterprise workflows rather than waiting for prompts from a human operator. Anthropic's Fable and Mythos models make a similar promise, advertising long-running autonomous capabilities across coding, research and knowledge work.

The direction is obvious. The destination is far less certain.

For all the excitement, software engineering remains the only domain where agentic AI has demonstrated consistent, measurable value. Writing code is easy. The hard part has become verifying it, reviewing it, testing it and deciding who owns the inevitable production outage when an autonomous agent confidently rewrites half your codebase. Even OpenAI's own messaging increasingly revolves around governance, secure environments and review pipelines rather than unsupervised automation (OpenAI to acquire Ona).

Then there's the bill.

The mythology says AI makes software cheaper. Reality seems intent on making it vastly more expensive. Reports of proposed 10-gigawatt data centres with estimated costs exceeding half a trillion dollars, enterprise concern over runaway inference spending and companies imposing internal usage caps on AI coding tools suggest that the biggest challenge facing agentic AI might not be intelligence at all. It might be the electricity meter (OpenAI in Talks to Lease 10 Gigawatt Ohio Data Center with Backing From Nvidia, 5 Ways Companies Keep AI Bills in Check, Uber Caps Usage of AI Tools Like Claude Code to Manage Costs).

Every autonomous loop burns more compute. Every retry burns more tokens. Every "thinking step" quietly lands on somebody's monthly invoice.

The safety conversation has evolved as well. It is no longer an abstract debate about alignment or existential risk. Anthropic's suspension of Fable and Mythos access following a U.S. government directive demonstrated that frontier models can now become geopolitical issues overnight. The practical concerns are suddenly mundane and deeply operational: audit logs, approval gates, prompt injection, credential scoping and legal liability.

None of this means the technology lacks value.

Far from it.

Banks, consultancies and regulated industries are deploying these systems at increasing scale, but they're doing so with the enthusiasm of a bomb disposal technician. BBVA, LSEG, KPMG and TCS are embracing AI through tightly governed enterprise programmes that emphasise trust, compliance and oversight rather than replacing entire departments with digital workers.

That may prove to be the defining story of 2026.

The real breakthrough isn't that agents can think for hours. It's that enterprises are discovering they must supervise them for just as long.

The hype machine still talks about replacement. The evidence increasingly points toward augmentation.

And perhaps that's the uncomfortable truth everyone keeps trying to ignore: artificial intelligence is starting to resemble every other employee. Useful. Expensive. Occasionally brilliant. Frequently unpredictable. And in constant need of management.