Key takeaways
ORI’s 30-second summary
- Software teams have moved from a single coding assistant to orchestrating several agents that write, test and review code.
- Speed went up, but specifications, review and architecture now decide quality more than typing speed ever did.
- Good teams log every agent action, gate merges on evaluations and keep a human accountable for each change.
- When you hire a partner, ask how they supervise agents — not just whether they use them.
Two years ago, "using AI" in a software team meant a developer accepting autocomplete suggestions in their editor. In 2026 it means something very different. A single engineer might hand a well-written ticket to one agent that drafts the change, a second that writes the tests, and a third that reviews the result against the team's standards — before a human ever opens the pull request.
The industry has started calling this agentic engineering. It is the biggest change to how software gets built since the move to cloud infrastructure, and it affects anyone who commissions software, not just the people writing it.
What actually changed
The shift is from writing code to directing work. Agents can now hold a task for long stretches: reading a codebase, running the test suite, fixing what breaks and trying again. The engineer's job moves upstream — deciding what should be built, breaking it into tasks an agent can complete, and judging whether the result is right.
- Implementation agents draft features and refactors from a ticket and the surrounding code.
- Test agents generate unit and end-to-end tests from the specification and the diff, then probe edge cases.
- Review agents check changes against style, security and performance rules before a human reviews them.
- Operations agents watch logs and error rates after a release and propose fixes for regressions.
Analysts expect this to keep spreading: Gartner has projected that around 40% of enterprise applications will include task-specific AI agents by the end of 2026. The same firm also warns that a large share of agentic projects will be cancelled — a reminder that the tooling is powerful but easy to misuse.
What did not change
Agents are fast, but they are only as good as the instructions and guardrails they are given. Three things matter more now than they did before:
1. Clear specifications
A vague ticket used to cost a developer an hour of clarification. Given to an agent, it produces a confident, complete and wrong implementation in minutes. Teams that write precise acceptance criteria get dramatically better results than teams that do not.
2. Architecture and judgement
Agents optimise locally: they solve the task in front of them. Deciding how services talk to each other, where data lives and what to leave out of version one is still human work — and it is where most long-term cost is won or lost.
3. Accountability
Every change that reaches production still needs a named person who understood it and approved it. "The agent wrote it" is not an answer a client, an auditor or an incident review will accept.
Our rule of thumb: agents can draft anything, but nothing merges without a passing test suite, an automated review and a human sign-off.
What a well-run agentic team looks like
From the outside, a mature team using agents looks calmer, not more chaotic. Inside, you will usually find the same set of practices:
- A shared library of prompts, skills and project rules, versioned like code.
- Evaluation gates in CI: generated code must pass tests, type checks, linting and security scans before review.
- Logged agent activity, so every change can be traced back to the ticket, the instructions and the reviewer.
- Least-privilege access: agents work in sandboxes and never hold production credentials.
- Small, frequent pull requests instead of large agent-generated dumps that nobody can review properly.
What this means if you are hiring a development partner
Almost every agency now says it uses AI. That claim alone tells you very little. The useful questions are about supervision and quality:
- How do you review AI-generated code, and who is accountable for each merge?
- What automated checks must a change pass before a human sees it?
- Where does our code and data go when an agent works on it, and is it used for training?
- How have your estimates and timelines changed since you adopted agents?
The last question is revealing. A partner that has genuinely adopted agentic engineering should be able to show you where it saves time — usually in tests, migrations, boilerplate and documentation — and where it does not, such as discovery, design and complex integrations.
The bottom line
Agentic engineering does not make software free, and it does not replace experienced engineers. It changes where their time goes: less typing, more thinking, more reviewing. For clients, that means faster iterations and lower cost on routine work — provided the team behind the agents has the discipline to keep quality high.