AI coding agents have made it trivial to get from an idea to a working prototype. Generating boilerplate, wiring up services or sketching out a feature is no longer the hard part. The difficulty shows up later, when that code has to survive contact with a real system and real users.
At that point, failures rarely come from the agent’s ability to write code. They come from something more basic. Nobody was fully aligned on what was being built, what ‘done’ actually meant or how the work was supposed to unfold. The agent fills in the gaps — it always does. The result is familiar: Overwritten tests, skipped edge cases and features that technically work but don’t solve the problem they were meant to address.
As AI-assisted development matures, teams start noticing a pattern. Better prompts help, but only up to a point. The difference between code that ships and code that gets reverted is usually the quality of the plan behind it. Prototypes tolerate ambiguity, but production systems don’t.
From working with agentic systems in real environments, four traits keep showing up in plans that actually hold.
Objectives Need a Finish Line
Humans are comfortable with loose goals. ‘Add authentication’ sounds fine in a meeting. An AI agent hears something else entirely. It has permission to decide what authentication means when it’s complete and how much effort is enough.
When success isn’t clearly defined, agents optimise for signals that look like completion. A clean diff. A confident explanation. Something that compiles. Those signals are cheap to satisfy and easy to mistake for correctness.
Compare the two:
- Add authentication
- Implement JWT authentication with 15-minute access tokens, refresh-token rotation and middleware that returns 401 responses for expired or invalid tokens
The difference isn’t verbosity; it’s testability.
A simple check helps here. If you can’t imagine how a reviewer or a test would clearly answer ‘yes’ or ‘no’ to whether the objective was met, the objective isn’t ready. Vague goals don’t just confuse agents; they invite shortcuts.
Structure Isn’t Enough — Tasks Have to be Runnable
Breaking work into smaller pieces is nothing new. Most teams already do this, often with tidy hierarchies of initiatives, epics and tasks. This structure helps people reason about scope, but it doesn’t guarantee that an agent can actually do the work.
A task like ‘improve the authentication system’ fits neatly into a plan and still leaves every important decision unresolved: Which files change? Which behaviour is expected to change? What assumptions are safe to make?
When those details are missing, agents either stop and ask questions endlessly or move forward on guesses. Neither scale.
Tasks that work are boringly specific. They spell out what’s in scope, what depends on what, which parts of the codebase are involved and how success will be judged. That’s not ceremony; that’s what turns intent into something executable.
Validation Can’t be an Afterthought
Language models are very good at sounding sure of themselves. They are much worse at knowing whether something actually works. Anyone who has watched an agent confidently declare success on code that doesn’t run has seen this firsthand.
In single-step tasks, that’s annoying. In multi-agent systems, it’s dangerous. One unchecked assumption becomes input to the next step, and errors spread quietly until the system behaves unpredictably.
The fix isn’t perfect verification; it’s discipline. Each step needs a clear way to prove it worked before anything downstream relies on it. If you can’t say how you’ll check the result, you shouldn’t automate the step.
Validation gates feel slow until you see what happens without them.
Plans Have to Change Without Resetting Everything
No plan survives implementation unchanged. Constraints surface. Priorities shift. Something that looked simple turns out not to be. What matters is what happens next.
Some teams lock plans down and force execution anyway. Others abandon the plan entirely and fall back to ad hoc prompting. Both approaches throw away useful context. One is rigid and the other has amnesia.
Good plans evolve. They keep what’s been learned and adjust what needs to change. Replanning shouldn’t mean starting over. It should mean refining the map as the terrain becomes clearer.
Why This Becomes Critical at Scale
AI agents execute quickly — that’s their strength. It’s also why weak planning hurts so much.
When execution is fast, mistakes compound faster, too. Context gets lost. Small misunderstandings turn into structural problems. The system doesn’t fail loudly; it drifts.
Speed alone isn’t the metric that matters anymore. The bottleneck has moved. It’s no longer typing or even generation — it’s clarity.
Planning is the Interface Now
In human teams, planning aligns people before work begins. In agentic systems, planning plays a similar role, but it’s closer to an interface than a meeting artifact. It’s how intent becomes something machines can follow without improvising.
Some tools are beginning to treat planning, validation and replanning as first-class engineering concerns rather than an informal setup. Artemis is one of the examples. But the point isn’t any specific platform — it’s the shift itself.
When execution is cheap, direction is everything.
The teams that get this right won’t just move faster, they’ll ship systems that still make sense a few months later, and that’s still the hard part.
