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AI coding agents can produce working code faster than teams can review it. That abundance does not prove the result is safe, maintainable or aligned with the business. Confidence comes from a delivery system that defines intent, limits what an agent may do, verifies changes independently and preserves evidence of the decisions.
Manish Kapur summarizes that idea as Intent × Guiderails × Verification × Evidence. It is a practical framework, not a measured score: if any factor is missing, strength in the other three cannot reliably compensate.
What the formula means
In his Daily Context article on DEV Community, posted October 12, 2026, Manish Kapur argues that AI makes code abundant while making confidence a systems problem. A pull request that compiles and passes a narrow test suite can still violate architecture, weaken security, duplicate existing logic or create expensive maintenance work. The surrounding workflow has to establish why the change exists, where an agent can operate, how the result is checked and what was learned.
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Intent × Guiderails × Verification × Evidence
The multiplication sign is deliberate. This is a conceptual model rather than a validated numerical equation, so it should not be used to calculate a confidence percentage or compare teams on a universal scale. It expresses a dependency: a beautifully specified task is still risky if the agent can alter production data without approval; strict permissions are not enough if nobody checks the output; excellent checks lose value when their results cannot be inspected later.
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| Element | Question it answers | What to define or retain |
|---|---|---|
| Intent | What outcome and standards must the change satisfy? | Business logic, security expectations, design patterns, performance needs, dependencies and local engineering conventions. |
| Guiderails | Where may the agent work, and where is a human decision required? | Allowed repositories, files, dependencies, data and actions; approval points and prohibited operations. |
| Verification | How will someone or something other than the generating model evaluate the change? | Repeatable checks for correctness, reliability, security, maintainability and organizational standards, run during the work. |
| Evidence | Can the team show what was checked, found and resolved? | Durable records of applicable standards, check results, findings, decisions, exceptions and resolutions. |
Why a successful pull request is not proof of confidence
A pull request is a collaboration and delivery mechanism, not a guarantee of a dependable outcome. Compilation demonstrates that the code satisfies a set of syntactic and type constraints. Tests demonstrate only the scenarios they cover. Neither automatically reveals an architectural boundary crossed by a new dependency, an authorization path that was missed, a duplicated rule that will diverge later or a design choice that conflicts with the rest of the system.
AI-generated changes can make this gap larger because the cost of producing another implementation is low. A team may accept a plausible patch before asking whether the requested behavior was specified precisely, whether an existing service already owns the logic or whether the change introduces a new operational obligation. More generated code means more surface area for review, not less.
Confidence therefore belongs to the whole delivery path. The useful question is not only “Does this branch pass?” but also “What did we intend, what was the agent allowed to touch, what independent checks ran, and what record can we inspect if the behavior is questioned later?”
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Intent is the contract for the work. Give the agent a goal that a reviewer can evaluate, along with the standards that constrain the solution. “Add a report export” is a starting point; a stronger brief states who may export, which records are included, the required format, expected performance, error behavior, retention rules and the existing component or service that should own the feature.
Include the business outcome
- State the user or operational problem and the observable result that counts as success.
- Identify what is explicitly out of scope so the agent does not turn an implementation task into an unapproved redesign.
- Describe compatibility requirements, such as existing API behavior, data migrations or client versions that must continue to work.
Make engineering standards visible
Kapur’s examples include business logic, security expectations, design patterns, performance needs, dependencies and existing engineering conventions. Put the relevant standards in the task, repository guidance or an agent-readable policy file. Point to the authoritative pattern rather than assuming the model will infer it from a large codebase.
- Business logic: define rules, edge cases and authoritative data ownership.
- Security: specify authentication, authorization, handling of secrets and treatment of untrusted input.
- Design: name the layers, interfaces, error conventions and patterns the change must follow.
- Performance: state the operation’s expected workload and unacceptable behavior, such as an unbounded query.
- Dependencies: identify approved libraries and licensing or maintenance constraints.
- Conventions: require the project’s formatting, naming, logging, testing and documentation practices.
Ask the agent to restate the intended outcome and constraints before editing. That response is not verification of the code, but it can expose an ambiguous requirement while the cost of correction is still low.
2. Set guiderails around the agent
Guiderails turn a broad instruction into a bounded operating space. They should describe both technical scope and decision authority. An agent may be able to read the whole repository while being permitted to modify only a specified package. It may prepare a migration but not execute it. It may suggest a dependency but require a human to approve adding it.
Define the operating boundary
- List the repositories, directories or services that may be read and changed.
- Specify whether generated files, infrastructure definitions, database schemas and deployment configuration are in scope.
- Limit network access, external data, credentials and production systems to the minimum necessary.
- State which dependencies, runtime versions and package registries are approved.
- Prohibit destructive operations, secret exposure, direct production writes and irreversible migrations unless a human explicitly authorizes them.
Mark human decision points
Not every decision can be delegated safely. Require approval for changes that alter public contracts, access control, retention, billing, regulated data, production infrastructure or irreversible state. A useful boundary is “prepare, explain and wait”: the agent can generate a migration plan and a dry-run result, while a person decides whether to apply it.
Guiderails should be testable. A reviewer should be able to see which files changed, which commands ran and whether the agent attempted an operation outside its grant. If the boundary exists only in an informal prompt, it is difficult to audit and easy to forget when work is transferred between people.
3. Verify independently and throughout the work
Verification evaluates the change; it is not a second request for the same model to approve its own output. Kapur recommends checks that are independent of the system that produced the code and that occur in the workflow rather than only at a slow final checkpoint.
Use several kinds of checks
| Check | What it can reveal | Typical timing |
|---|---|---|
| Behavioral tests | Incorrect results, missing edge cases and regressions in specified scenarios. | After each coherent change and in continuous integration. |
| Static and dependency analysis | Unsafe patterns, vulnerable or unapproved packages, type errors and policy violations. | As files or dependencies change, before review. |
| Architecture and maintainability review | Layer violations, duplicated logic, unclear ownership and unnecessary complexity. | During design review and pull-request review. |
| Security review | Authorization gaps, injection paths, secret handling problems and unsafe data flows. | For every security-sensitive path, not only at release. |
| Operational checks | Logging, metrics, failure recovery, resource use and deployment compatibility. | Before merge and in an environment resembling production. |
Independence does not require a different vendor or a particular tool. It means the check has a distinct basis for judgment: a deterministic test, a policy rule, a separate reviewer, a security analysis or an architectural standard that the generating model cannot silently redefine. A second model can contribute, but its output should not be treated as decisive evidence by itself.
Move checks earlier
Run fast checks while the agent is still working: formatting, type checks, focused tests and policy validation can shorten the feedback loop. Use broader integration, security and operational checks before merge or deployment. Early failures prevent a wrong assumption from spreading across many files, while later checks cover interactions that are invisible in a narrow unit test.
When a check fails, require the agent to explain the failure and propose a bounded correction. A human should decide whether the correction addresses the root cause or merely changes the test, suppresses a warning or broadens a permission.
4. Preserve evidence that survives the chat session
Evidence is the durable trail connecting intent to outcome. Keep the standards that applied, the checks that ran, the findings they produced and the disposition of each finding. This lets a reviewer understand a change without reconstructing an ephemeral conversation and gives the team material for incident analysis or future maintenance.
What a useful record contains
- The original objective, scope and acceptance conditions.
- The agent’s permitted repositories, files, data and actions, including approval requirements.
- The files, dependencies and configuration changed.
- Commands, test suites, analyses and review steps that were run, with their results.
- Findings that were fixed, accepted as exceptions or deferred, including the responsible decision-maker and rationale.
- Migration, rollout, monitoring and rollback information when the change affects operations.
Store this information with the change wherever possible: a pull-request description, linked build records, policy results and review comments are more useful than an unsearchable transcript. Retain enough context to reproduce the reasoning, while following the organization’s rules for secrets and personal data. Evidence is not a demand to preserve every model token; it is a record of decisions and verifiable results.
A practical workflow for applying the four factors
The framework does not prescribe one universal rollout sequence, but teams can turn it into a repeatable workflow. Adjust the gates to the risk of the change rather than applying identical ceremony to every edit.
- Write the change brief. Record the outcome, in-scope behavior, exclusions, standards, dependencies and acceptance conditions.
- Declare the boundary. List permitted repositories and actions, prohibited operations and the points that require human approval.
- Ask for a plan first. Have the agent identify affected components, reuse opportunities, risks and checks before it edits files.
- Implement in small slices. Keep changes coherent and reviewable. Run fast automated checks after each slice.
- Inspect the diff. Compare the implementation with the stated intent, not only with the test output. Look for unrelated edits, duplicated rules, new permissions and dependency changes.
- Run independent verification. Execute the project’s relevant behavioral, static, security, architecture and operational checks. Record exact results.
- Resolve findings explicitly. Fix, reject, defer or accept each issue with a reason and an owner. Do not silently suppress a failing check.
- Obtain the required human approvals. The designated person decides on security-sensitive, irreversible or externally visible changes.
- Package the evidence. Attach the brief, boundary, checks, findings and resolutions to the durable change record before merge or deployment.
How to spot a weak implementation
| Weak pattern | Why it reduces confidence | Stronger practice |
|---|---|---|
| “Make the tests pass” is the entire prompt. | Tests may omit business rules, security and architecture. | State the outcome and applicable standards before implementation. |
| The agent can edit any repository or run any command. | Scope creep and irreversible actions become possible. | Allow-list locations and actions; require approval for sensitive operations. |
| The generating model reports that its code is correct. | The evaluator shares the producer’s blind spots. | Use independent, repeatable checks and human review where risk warrants it. |
| All checks run only at the release gate. | Feedback arrives late, after assumptions and edits have multiplied. | Run fast checks during work and broader checks before merge. |
| A green status is retained without the underlying findings. | The team cannot explain what was tested or why exceptions were accepted. | Preserve results, decisions and resolutions as evidence. |
Questions to put in every agent-assisted change review
Kapur’s closing prompts make a compact review checklist:
- Did the agent know the relevant standards before it started?
- What was it allowed to touch, and which decisions needed a human?
- Did something other than the model that wrote the change check it?
- Can the team show what was found and how each issue was resolved?
Answer these questions with links or records in the change system, not with assurances in a meeting. If an answer is “no,” the remedy is usually to strengthen the missing factor before increasing the agent’s scope.
Choosing the right level of control
Controls should match consequence. A documentation edit may need intent, a limited file boundary and a basic review. A payment authorization change needs precise business rules, strict permissions, security and regression checks, explicit approval and a durable audit trail. The formula stays the same; the depth of each factor changes with risk.
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Do not treat more process as automatically better. Excessive approval for low-risk work can encourage bypasses, while vague controls for high-risk work create false confidence. Start by classifying the consequences of an error, then select the standards, boundaries, checks and evidence needed to make that risk visible and manageable.
What this framework does—and does not—claim
The four factors provide a way to reason about confidence in agent-assisted delivery. They do not establish an industry-wide defect rate, a productivity multiplier or a universal threshold for release. The source presents no named statistic or study result, and it does not prescribe particular tools, models, thresholds or a single adoption plan.
That limitation is useful: teams can implement the ideas with the systems they already operate, then refine their standards based on incidents, review findings and operational experience. The goal is not to assign a score to an AI-generated patch. It is to make the path from requested outcome to deployed behavior understandable, bounded, independently checked and learnable.
Bottom line for engineering teams
AI can increase the volume and speed of code production, but production confidence does not come from volume or speed. Define intent before generation, constrain the agent’s authority, verify changes with checks that do not depend solely on the generating model and retain evidence of findings and decisions. When any one of those elements is absent, a green build can still hide a costly surprise.
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