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AI coding agents can produce working code quickly, but a completed pull request is not proof that the resulting system is dependable. Confidence comes from four connected controls: clear intent, explicit guiderails, independent verification, and durable evidence. Manish Kapur presents that relationship as Intent × Guiderails × Verification × Evidence—a conceptual model in which a serious weakness in any one factor undermines the whole outcome.
What the confidence formula means
The formula comes from Manish Kapur’s opinion article, “Code is abundant. Confidence is a formula,” published by Daily Context on DEV Community on October 12, 2026. It is not a mathematical score, a benchmark, or a validated prediction. The multiplication sign is a warning about dependency: strong performance in three areas cannot reliably compensate for a missing fourth area.
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In practical terms, an agent may generate syntactically valid code, pass a narrow test suite, and still create an unacceptable change. It might violate an architectural rule, weaken a security boundary, duplicate an existing capability, or create maintenance work that appears only months later. The formula shifts attention from the amount of code produced to the conditions under which that code is accepted.
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|---|---|---|---|
| Intent | The goal and standards the change must satisfy | What outcome are we asking for, and what constraints matter? | The agent optimizes for a vague request or a local fix that conflicts with system goals |
| Guiderails | The boundaries of the agent’s authority | What may it read, change, or execute, and where is approval required? | The agent can touch unrelated repositories, dependencies, data, or actions without a defined human decision point |
| Verification | Independent evaluation of the change | What checks will examine correctness, reliability, security, maintainability, and organizational standards? | The same model that wrote the code is effectively the only reviewer |
| Evidence | A durable record of standards, checks, findings, and resolutions | Can another person reconstruct what was checked and why the result was accepted? | A team has a merge decision but no inspectable record of the reasoning or unresolved risk |
Why abundant code does not create confidence
Traditional development already requires more than producing source files. A change has to fit business logic, security expectations, design patterns, performance needs, dependencies, and the conventions of the existing engineering organization. An AI agent can accelerate the first act—writing code—without automatically understanding all of those surrounding obligations.
A green build proves only what the build and its tests actually exercise. A narrow suite may miss an architectural violation. Compilation does not demonstrate that authorization is enforced on every path. A passing unit test does not reveal unnecessary duplication or a design that will make future changes expensive. A pull request records that code was proposed and perhaps merged; it does not, by itself, demonstrate that the relevant risks were considered.
This distinction matters because speed changes the volume and cadence of change. When code is cheap to produce, review capacity, policy clarity, and traceability become the limiting factors. The useful question is not “Can the agent write this?” but “What must be true before this change is allowed into the system, and how will we know?”
Start with intent before the agent starts
Define the outcome, not just the edit
Intent is the specification of success. It should state the user or business outcome, the part of the system involved, and the behavior that must remain unchanged. “Add an endpoint” is an edit. “Allow authenticated account owners to export their records in the existing format without exposing another account’s data” is an intent statement that gives an agent and its reviewers something testable to reason about.
The level of detail should match the risk. A small text change may need little more than the desired wording and location. A change that handles personal data, payment state, permissions, or shared infrastructure needs explicit behavioral and safety expectations before generation begins.
Make standards visible and specific
Kapur’s examples of relevant standards include business logic, security expectations, design patterns, performance needs, dependencies, and existing engineering conventions. Capture the standards that apply to the task in the work item or agent brief rather than assuming the model will infer them from a few nearby files.
- Business logic: State allowed states, transitions, edge cases, and invariants.
- Security: Identify authentication, authorization, data-handling, and secrecy requirements.
- Design: Name the architectural layer, pattern, or interface the change must use.
- Performance: Describe latency, throughput, resource, or query behavior that must be preserved.
- Dependencies: Identify approved libraries, compatibility limits, and interfaces that cannot change.
- Conventions: Point to the repository’s established naming, error-handling, testing, and observability practices.
Resolve ambiguity explicitly
If two standards conflict, decide which one governs before generation. If the request leaves a material choice open, mark that choice as a human decision instead of allowing the agent to silently choose. Clear intent reduces rework because reviewers can evaluate the result against an agreed target rather than negotiating the target after code exists.
Use guiderails to bound the agent’s authority
Define what the agent may touch
Guiderails are operational boundaries. They can cover repositories, directories, dependencies, data, commands, and external actions. A task should identify the smallest scope that permits useful work. If an agent needs one service and its tests, it should not automatically have authority over unrelated services or deployment configuration.
Boundaries should be understandable to a human reviewer and enforceable in the workflow. State which files or components are in scope, which dependencies may be added or upgraded, and which data may be read. Treat access to production systems, credentials, customer data, and irreversible operations as separate decisions rather than implied permissions.
Mark human approval points
Some actions should pause for a human decision: changing a public interface, altering authorization, adding a dependency, modifying a data schema, deleting data, or executing an external operation. The exact approval points depend on the organization and the change, but they should be declared before the agent reaches them.
A useful boundary separates preparation from commitment. An agent may draft a migration, show a dependency diff, or propose a configuration change; a person can then approve the action that applies it. This preserves speed for exploration while reserving consequential decisions for accountable owners.
Limit scope without making the agent ineffective
Overly broad permissions increase the blast radius of a mistake. Overly narrow permissions can force unsafe workarounds or prevent the agent from seeing necessary context. Start with the minimum useful scope, expand it only when the task requires more context, and record the reason for each expansion. Guiderails are not a substitute for review; they make review tractable by controlling what needs to be examined.
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Use checks that match the risk
Verification evaluates correctness, reliability, security, maintainability, and organizational standards. No single check covers all of them. Select checks according to the intent and the affected surface: behavioral tests for stated outcomes, interface checks for compatibility, security review for trust boundaries, and maintainability review for duplication or architectural fit.
The important distinction is independence. The model that generated a change should not be the only authority declaring that change correct. Independent verification can be performed by different tests, static analysis, a separate reviewer, or a process that evaluates the result from the stated standards rather than from the model’s own explanation. The particular mechanism is an organizational choice; the independence requirement is the safeguard.
Place feedback close to generation
Run meaningful checks as the work progresses instead of waiting for a slow final checkpoint. Early feedback lets the agent correct a misunderstanding while the relevant context is still active. It also prevents a large batch of changes from hiding the origin of a failure.
Verification should examine both the changed behavior and its interaction with the surrounding system. A change can satisfy a new unit test while breaking an existing contract, bypassing a policy, or introducing a second implementation of an existing capability. Review the diff, affected interfaces, and relevant conventions—not only the newly added lines.
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When a check fails, classify the finding: incorrect behavior, reliability risk, security weakness, maintainability problem, or violation of an organizational standard. Require a recorded resolution, which may be a code change, an intentional exception approved by the right owner, or a decision not to proceed. Do not treat a model’s confidence or a plausible explanation as proof that the finding is harmless.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Preserve evidence that survives the conversation
Record the standard and the check
Evidence is the durable account of what the team expected and what it observed. Keep the applicable intent and standards with the change, along with the checks performed and their results. A future maintainer should be able to tell which requirements were considered without recovering an old chat session.
Record findings and resolutions
For each material issue, preserve what was found, who or what found it, the decision taken, and how the issue was resolved. If the team accepts a risk, record the rationale and the owner of that decision. If the code changes in response, retain the connection between the finding and the resulting revision.
Make evidence useful for learning
Evidence is not paperwork for its own sake. Patterns in findings can reveal unclear standards, overly broad permissions, missing checks, or recurring design problems. Reviewing those patterns helps a team improve the delivery system instead of merely correcting individual agent outputs. The record also makes incident analysis and later maintenance more grounded because the original assumptions are visible.
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A practical workflow for applying the four elements
- Write the intent. State the desired outcome, affected behavior, constraints, and applicable business, security, design, performance, dependency, and convention requirements.
- Set the guiderails. List repositories, files, data, dependencies, and actions in scope. Identify prohibited areas and the exact decisions that require human approval.
- Ask the agent to plan against those conditions. Require it to identify assumptions, affected interfaces, and uncertainties before making broad changes. Resolve material ambiguity with a person.
- Generate in reviewable increments. Keep changes small enough that a reviewer can connect each modification to the stated intent and its boundaries.
- Run independent verification during the work. Apply checks for behavior, reliability, security, maintainability, and organizational standards as soon as the relevant code exists.
- Inspect the surrounding system. Look for architectural violations, duplicated capabilities, dependency effects, policy bypasses, and maintenance costs that a narrow test or successful compilation may miss.
- Resolve and document findings. Link each material finding to its fix, approved exception, or decision to stop. Preserve the check results and the reasoning.
- Make the final decision. Accept the change only when the evidence shows that the stated intent was met within the guiderails and that material verification findings have an explicit resolution.
How to diagnose a weak delivery system
| Observed symptom | Likely missing element | Corrective question |
|---|---|---|
| The agent produces plausible code but solves the wrong problem | Intent | Were the outcome, constraints, and applicable standards written before generation? |
| Unrelated files, services, or dependencies change unexpectedly | Guiderails | What was the permitted scope, and which expansion required approval? |
| Only the generating model’s explanation supports acceptance | Verification | What independent check evaluated the change against the required qualities? |
| The team cannot explain why a risky change was accepted | Evidence | Can someone inspect the standards, findings, decisions, and resolutions now? |
| Tests pass but later maintenance or architecture problems appear | Verification and intent | Did checks cover system-level standards and long-term maintainability, not just local behavior? |
What the formula does not promise
The framework does not provide a universal score, a required toolchain, or fixed thresholds for approval. It does not claim that every agent-generated change is unsafe, nor that human review alone guarantees quality. It is a way to expose dependencies in the delivery system: clear goals without boundaries can still permit excessive access; strict boundaries without verification can preserve an incorrect change; extensive checks without evidence leave the organization unable to explain its decision.
The four elements also do not eliminate engineering judgment. Teams still have to decide which standards apply, how much verification is proportionate to risk, and who owns an exception. The value is that those decisions become explicit and repeatable instead of being hidden inside a model conversation or an informal review.
Questions to ask before approving an agent-generated change
- Did the agent know the relevant business, security, design, performance, dependency, and organizational standards before it started?
- What repositories, files, dependencies, data, and actions was it allowed to touch?
- Which decisions required a human, and is there a record of those approvals?
- Did something other than the model that wrote the change check it?
- Were checks run during the work as well as at the final decision point?
- Can the team show what was found, how each material issue was resolved, and why any accepted risk was acceptable?
These questions are the operational form of the formula. If any answer is unclear, the problem is not necessarily the generated code; it is a missing part of the system that turns abundant code into dependable software.
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