AI agents write code incredibly fast. They destroy software architecture even faster.
That's not a secret anymore. It has a name now — agentic technical debt — a stack of studies behind it, and a rough consensus: autonomous agents accumulate architectural debt faster than any review process can absorb it.
Drop a standard AI coding agent into a complex, legacy-integrated enterprise environment and you will get speed. But you pay for it — quietly breaking dependency-injection boundaries, coupling modules that were designed to stay independent, and seeding architectural drift that won't surface until the next major release.
The diagnosis is everywhere now. The fixes are arriving fast too. The question worth asking isn't whether agents drift — that's settled — but what a mechanism has to guarantee before you'd trust it with a regulated codebase.
I work on software in regulated domains — mixed C++, .NET Framework, native modules, where a defect outlives the sprint that shipped it. In environments like that, "lines of code generated" is the wrong thing to optimize. The right question is narrower and harder:
How deterministically can you constrain the agent — and how do you prove the constraint held?
Everything below is the answer to that question.
Multi-pass, not one-shot prompting
Most teams hand an agent a user story and ask for a pull request. That is a recipe for hallucination — AI amplifies a vague spec instead of catching it.
The fix is a mandatory Context Interrogation pass. Before the agent writes a single line, it reads the ticket, scrapes the repository context, and generates structural questions about what it doesn't understand.
Then it stops. It waits for the engineer to answer. Only when the ambiguity is resolved does it move to implementation. The agent has to prove it understands the constraints before it is allowed to act on them.
And implementation itself is never one agent's word against the codebase. A builder pass produces the change; an independent verifier pass — a separate agent with no stake in the draft — audits it against the spec and the risk tier before a human ever opens the diff. The builder proposes. The verifier prosecutes. The human judges.
Hard boundaries beat good intentions
You cannot protect a complex C++ monolith with a system prompt that says "please don't break the DI container." The model will eventually ignore it.
The boundary has to be structural. Agents execute through specific, constrained CLI commands anchored to the build pipeline — not open-ended access to the repo. Instructions stay decoupled from the architecture documentation: the agent reads the exact same ARCHITECTURE.md as the human engineers, proposes a change against it, and then the system halts for a human stop gate.
One more distinction most teams blur: a rule that lives only in documentation is a convention, not a control. If the pipeline doesn't mechanically check it, you're back to trusting good intentions — just better-formatted ones. The rule has to graduate from markdown to a merge check before you get to call it governance.
A prompt is a suggestion. A gate is a wall.
Risk has two axes
This is where most governance frameworks quietly fail. They collapse "risk" into a single dial, then over-gate trivial work while waving dangerous work straight through.
There are two independent questions here, and they need two separate taxonomies.
Action risk is about the operation. Reading a file is not deleting a branch, which is not rewriting the build pipeline. Action risk governs what the agent is allowed to do on its own.
Change risk is about the consequence. Editing a log message is not touching the calculation core of a regulated system. Change risk governs how many gates a change has to clear before it ships.
The reason you need both is the mismatched cells. An agent might want to modify the pipeline — operationally frightening, but the change itself is a one-line lint step. Gate the action, then let it through. The opposite case is the dangerous one: a single-file edit, the most boring operation there is, that happens to land in the calculation core. A single-axis model waves that through because the operation looked small. The two-axis model stops it, because the consequence is enormous no matter how trivial the keystrokes were. Separate the axes and your friction finally lands where the danger actually is.
The death of the dirty draft
The industry has largely accepted that AI generates a "dirty draft" and the human cleans it up. In a regulated environment, cleaning a dirty draft often takes longer than writing it from scratch — because you have to verify every hidden assumption the AI made.
Demand tests that prove the behavior changed the way the spec intended — not just that lines got executed — before the human gate unlocks, and the dirty draft disappears. You stop reviewing guesses. You start reviewing verifiable, production-ready proposals.
You test the code. Who tests the agent?
Here's the question almost nobody in the agentic tooling conversation is asking: the agent's output gets tested — but what tests the agent itself?
Every agent capability in this system is a named skill, and skills don't just ship. They graduate.
A skill starts life as experimental. To earn broader autonomy it has to pass an evaluation gate: a suite of golden cases drawn from real work items, scored two ways at once. Deterministic assertions catch the structural failures a machine can check — did the verdict field exist, did the risk tier appear, did the agent claim to have edited code it was never allowed to touch. An LLM judge scores what a regex never can — was the tier classification actually right, was the reasoning sound, would a senior engineer accept this rationale.
Both halves are enforced by validators, and the validators themselves are validated — fixture contracts, sample reports, evidence recorded against the skill's registry entry. When a skill is updated, the golden cases become its regression net. A prompt tweak that silently degrades the skill's judgment fails the gate before it ever touches a real ticket.
That's the shift: trust in the agent stops being a feeling and becomes a maturity state with evidence behind it. Unit tests protect the codebase from the change. Evals protect the codebase from the agent.
Gates that remember
Deterministic boundaries and risk axes secure the perimeter. They are not enough to scale.
The outcome of every stop gate is information, and most teams throw it away. A human blocks a change, types a reason, merges a corrected version — and the next time the agent is summoned, it starts from zero.
It shouldn't. This system gives the agent one unified memory: a single governed record store with exactly two operations — Capture and Recall. When a human blocks a pass, the verdict is captured as a record. When the agent picks up the next ticket, it doesn't only read ARCHITECTURE.md — it recalls the log of why its last three passes were stopped.
But — and this is the part that separates a memory from a liability — the memory itself is gated. Every record carries its provenance: what kind of learning it is, how strong its source was, and which gate authorized it — an explicit human approval is not the same as an inference from an existing canonical document, and the schema refuses to let the two blur. An agent that can freely write to its own memory will eventually launder its mistakes into policy. An agent whose memory writes pass through the same gate discipline as its code cannot.
That is the difference between a tool you re-explain your architecture to every morning and a system that internalizes your judgment — without ever being allowed to invent it. Constraint that compounds.
Looks governed is not governed
One last trap, because it's the one waiting at the end of everything above.
You can build every mechanism in this article — the gates, the axes, the evals, the memory — and still be running on faith, because a governance framework that only exists in documents is a claim, not a fact. The failure mode of mature teams isn't lawless agents. It's beautifully documented controls that nobody can prove fired.
So every skill run emits a telemetry record: what ran, against which work item, at what risk tier and action level, what evidence the verifier counted, whether a mutation was proposed and whether a human approved it, what the gate decided and why. Not logs to grep when something breaks — a structured, append-only account of the system governing itself.
That record is what turns "we have a governed process" from an assertion into an audit trail. It's what a quality team can inspect, what a regulator can trace, and — bluntly — what sustains an adoption mandate through its first budget review. Speed wins the pilot. Proof wins the program.
The real bottleneck
Scaling an agentic adoption isn't about finding a smarter model. It's about building a stricter factory — one that tests its own machines, remembers its own verdicts, and keeps the receipts.
Governance isn't red tape. It's the guardrail that lets you drive fast. The teams that win this transition won't be the ones with the most autonomy. They'll be the ones whose orchestration layer turns every human "no" into the agent's next constraint — and can prove, run by run, that the constraint held.
The bottleneck was never generation. It was building a system that learns from the gate.
