Executable specs for agentic coding: how to build a verification layer

Executable specs turn product requirements, design constraints, and runtime behavior into machine-checkable inputs that coding agents can verify before code is merged. Instead of trusting generated code because it compiles, teams validate whether a feature matches intent, works in a real preview env

MMichael SwindellJune 17, 202617 min read

Executable specs turn product requirements, design constraints, and runtime behavior into machine-checkable inputs that coding agents can verify before code is merged. Instead of trusting generated code because it compiles, teams validate whether a feature matches intent, works in a real preview environment, and preserves critical product flows. In agent-first teams, this matters because implementation is getting cheaper while review and verification remain the main bottleneck. A practical setup combines specs from ticketing tools, deployed application state, and codebase context, then routes those inputs through a verification workflow. Platforms like Levr fit this model by giving coding agents shared project context and verification gates.

At a Glance

A practical architecture for spec-driven agent verification

Faster code generation only helps if teams can trust what actually shipped.

● Core problem: Coding agents increase PR volume, but human review remains the bottleneck when teams cannot trust implementation to match specs.

● Architecture: Separate planning from verification, then split verification across many focused sub-agents instead of one overloaded agent.

● Critical inputs: Good results come from combining specs, design context, deployed preview environments, and base-branch code.

● Security model: Runtime checks against unknown preview URLs should run in isolated, ephemeral sandboxes rather than in your primary cloud environment.

● Best use case: Validate high-value product flows, feature acceptance criteria, and regressions that matter most to customers and revenue.

Verification improves when specs stop being documentation only and start acting like execution inputs.

Why is verification now the bottleneck in agentic coding?

Verification is the bottleneck because coding agents can generate implementation faster than teams can confirm correctness, intent, and safety. The result is more pull requests, more waiting, and lower trust. Without a verification layer, velocity often shifts from writing code faster to reviewing more uncertain output more slowly.

That pattern shows up across small teams and large engineering organizations alike. More code is being produced, but the confidence needed to merge it has not scaled at the same rate.

The bottleneck is not just code quality in the narrow sense. It is product correctness. A feature can compile, pass unit tests, and still miss the actual requirement. It can ignore an important design constraint, break a key onboarding flow, or subtly diverge from how product and design intended the feature to work.

This gap between implementation and intent is exactly why trust becomes the dominant issue. According to the Stack Overflow Developer Survey 2024, developers are already using AI tools broadly, but trust and accuracy concerns remain central barriers to relying on output without close review. More generated code without stronger verification just increases review load.

What is an executable spec in practice?

An executable spec is a requirement artifact that agents can parse, reason over, and verify against running software. It is not only prose in a ticket. It includes structured acceptance criteria, design expectations, and enough context to test whether a delivered feature matches the intended behavior.

In practice, that means the spec becomes an input to the delivery system, not a reference document people skim and forget. The verification workflow extracts requirements, turns them into checkable items, and evaluates them against the preview or staging version of the product.

A useful executable spec usually draws from several sources:

Ticketing systems such as Jira, Linear, monday.com, Notion, or GitHub issues.
Acceptance criteria and product notes.
Design assets and visual rules.
The deployed feature in preview or staging.
The relevant codebase context.

This matters because code-to-code comparison is not enough. Many failures only appear when the feature is running in an environment connected to the real UI, navigation, state, integrations, and related flows.

How do you design a verification architecture that agents can actually run?

A workable verification architecture separates requirement extraction from runtime checking, then distributes the checking work across many focused agents. This avoids context overload and makes verdicts more consistent. One agent plans what to verify, while other agents validate each requirement independently and report back.

The architecture described in the source material has three functional layers:

Spec ingestion from ticketing systems and design sources.
Verification planning that extracts requirements and likely failure cases.
Secure runtime execution that checks the real feature in an isolated environment.

The simplest mental model is:

A planner reads the spec.
~~It extracts concrete requirements.~~
~~Each requirement is assigned to a focused verifier.~~
An orchestrator collects verdicts and produces a final report.

For teams using an agent-first control plane, one way to organize this is inside Levr’s workflow model: define intent in natural language, let agents pick up tasks, run automated verification, then route verified outputs for human approval. The key point is not the product. It is the control plane pattern. Verification needs to be part of the workflow, not an afterthought.

Why does a single verification agent fail on real product specs?

A single verification agent fails because the task mixes too much context: long specs, design details, codebase state, UI interaction, and multiple acceptance criteria. As context grows, agents skip requirements, become inconsistent, or stop running entirely. Verification quality drops before teams notice why.

This failure mode is easy to underestimate. It feels reasonable to ask one model to read a ticket, inspect the UI, compare behavior to design, and produce a verdict. In reality, this can blow up the context window or degrade performance enough that the output becomes unreliable.

Typical symptoms include:

Some requirements never get checked.
Verdicts vary from run to run.
The agent invents issues nobody asked about.
Long sessions become less accurate as they progress.

This is a context engineering problem, not just a prompting problem. Even with larger context windows, complex multi-step tasks can become weaker when old, irrelevant information keeps accumulating. Official model documentation from providers such as OpenAI also makes clear that tool use, multi-step reasoning, and context management still require careful orchestration rather than brute-force token expansion.

Give agents verification context, not just coding prompts

If your team is already using Claude Code, Cursor, Codex, or Copilot, a shared control plane helps bind specs, issues, tests, and review gates into one workflow.

See how teams structure this →

How should you split planning and execution for better verification?

Split planning and execution by assigning one agent to extract and normalize requirements, then separate agents to verify those requirements against the product. This reduces cognitive load per agent and improves coverage. It also makes debugging easier because failures happen in smaller, inspectable units.

The planner’s job is narrow but important. It should:

Read the ticket or PRD.
Identify all requirements.
Extract likely failure cases.
Produce a verification checklist.

The verifier’s job is different. It should:

Take one requirement at a time.
Inspect the relevant runtime behavior.
Check design or UI constraints where needed.
Return a verdict with evidence.

This planner and executor split is portable. It can work whether your specs live in Jira, Linear, or GitHub, and whether your runtime checks use a browser session, design inspection, or both.

Why do sub-agents improve requirement coverage?

Sub-agents improve coverage because each agent handles one requirement or one small cluster of checks in isolation. That keeps context short, avoids contamination from earlier steps, and makes the agent more focused. Parallel execution also reduces verification time while producing cleaner, requirement-level verdicts.

A common failure in sequential verification is accumulated irrelevance. By the fifth or sixth requirement, the session contains a large amount of old reasoning that no longer helps. That history can make the model slower, less precise, and more likely to miss later requirements.

The better approach is:

Extract all requirements.
Spawn one verifier per requirement.
Run them in parallel where possible.
Collect all verdicts in an orchestrator.

This is where infrastructure like Levr becomes relevant for agentic teams. Message buses, shared memory, and intelligent routing are useful when many verification agents need to coordinate without passing brittle markdown artifacts around manually.

What inputs produce the most reliable verification results?

The most reliable results come from combining specs with codebase context and runtime access to the deployed feature. Specs define intent, but code grounds the agent in reality. Without both, verification drifts toward either blind literalism or invented assumptions about what the feature should do.

One of the most important lessons in the source material is that giving agents only specs led them to invent requirements that no one had requested. That happened because the model understood the goal, but not the actual codebase constraints and implementation reality.

Two practical refinements stood out:

Use the base branch, not only the diff. Reviewing just the diff can bias the verifier toward the chosen implementation instead of evaluating whether it was the right implementation.
Scope verification by domain. If the feature is front-end only, avoid irrelevant back-end review paths that add noise.

For design-heavy work, the metadata also points to Figma integration through Model Context Protocol, or MCP. That makes sense for validating spacing, typography, colors, and layout rules programmatically when visual correctness is part of the spec.

How do you verify UI behavior instead of just code?

To verify UI behavior, the agent must inspect a running preview or staging environment and interact with the product as a user would. Code inspection alone cannot confirm layout, navigation, visual overlap, or real runtime state across connected flows.

This is the key distinction between implementation review and feature verification. A front-end issue like a misplaced button can be obvious in a deployed preview and invisible in a code diff unless the reviewer already knows exactly where to look.

Useful runtime checks include:

Button placement and visual overlap.
Color, typography, and spacing expectations.
Resolution and layout behavior.
Navigation and click flow correctness.
Integration activation and onboarding behavior.
Payment, signup, SSO, and subscription flows.

This is also why end-to-end style exploration matters for certain high-value paths. A feature can satisfy its direct acceptance criteria but still break a critical commercial flow nearby.

How should teams think about regression coverage with agent verification?

Teams should use agent verification to protect the small set of product flows that matter most, not to exhaustively roam every screen on every pull request. The best targets are core journeys, fragile integrations, and revenue-critical actions that break expensively when they fail.

The source material describes using the system for repeated checks across important flows such as dashboard integrity, integrations, onboarding, subscription, Stripe payment, Google account signup, and SSO. That is a sensible strategy.

Instead of treating this like universal exploratory testing, prioritize:

Core onboarding paths.
Billing and subscription.
Authentication and SSO.
Key integrations.
Any flow whose failure directly affects revenue or retention.

This aligns with broader engineering guidance from sources like GitHub Octoverse, which consistently shows that software delivery performance depends on managing the full workflow, not only code generation speed. Verification effort should be concentrated where its business impact is highest.

How do you secure runtime verification against unknown preview environments?

Secure runtime verification by isolating browser sessions and runtime checks in ephemeral sandboxes with narrow permissions. Agents should not browse unknown preview URLs from the same environment that holds sensitive credentials, storage, or customer data. Isolation is a security requirement, not a nice-to-have.

This is one of the easiest places to take an unnecessary risk. If your verification agent is opening arbitrary customer-provided URLs, it should not run in the same trusted environment that stores core infrastructure secrets.

The architecture described uses a third-party approach with AWS Agent Core and an ephemeral sandbox per requirement. The broader principle is what matters:

One isolated session per validation task.
Permission-bounded runtime.
Short-lived execution environment.
Result sent back to the main orchestrator only after the check completes.

If your team is weak on browser isolation, sandboxing, or secure agent execution, use proven third-party tooling rather than improvising. Verification logic is a productive place to innovate. Security boundaries are usually not.

How do you implement a spec-driven workflow with Levr?

A practical way to implement this in Levr is to connect issues, specs, coding agents, and verification gates into one control plane. That keeps requirement context, task state, test evidence, and review history attached to the same project rather than scattered across separate tools.

One way to structure it in Levr is:

Define intent in natural language. Capture the feature as a project or issue with acceptance criteria, known edge cases, and design references.
Let coding agents pick up the task. Claude Code, Cursor, Codex, or Copilot can work from the shared issue context instead of a detached prompt.
Run automated verification. Attach spec-derived checks and high-priority regression flows as gates before merge. Levr’s projects, tests, and observability features map naturally to this stage.
Review evidence, not only diffs. Human reviewers can approve with verdicts tied to the issue, feature context, and test history.

This kind of control plane matters more as you add multiple agents. Once planners, verifiers, and coding agents all participate, you need shared memory, task routing, and a clear gate model. Otherwise, agent output scales faster than team understanding.

Technical Deep Dive FAQ

What is a verification layer for agentic coding?

A verification layer is the system that checks whether code produced by coding agents actually matches product intent, design constraints, and runtime expectations. It sits between implementation and merge approval. Instead of assuming generated code is correct because it compiles or passes a small test set, the verification layer evaluates the feature against specs, design artifacts, and real behavior in preview or staging environments. Its job is to increase trust, reduce review bottlenecks, and catch mismatches before they reach production.

What are executable specs?

Executable specs are requirements that can be consumed directly by automated systems for validation. They usually begin as tickets, PRDs, or design notes, but they are normalized into structured inputs that an agent can reason over and verify against running software. A strong executable spec includes acceptance criteria, relevant design expectations, and enough operational context to determine whether a feature behaves correctly in a real environment. The goal is to transform specs from passive documentation into active verification inputs.

Why not just rely on unit tests and PR review?

Unit tests and pull request review are necessary, but they often miss product-level mismatches. A feature can pass tests while still violating a design rule, breaking onboarding, or misunderstanding a requirement hidden in a long ticket. Human PR review also scales poorly when coding agents increase output volume. A verification layer complements those practices by checking runtime behavior and spec alignment directly. It focuses less on whether code is merely valid and more on whether the implementation is actually the feature the team intended to ship.

Why is separating planning from execution so important?

Planning and execution demand different kinds of reasoning. Extracting requirements from a vague or inconsistent spec requires a strong model that can normalize intent. Verifying one concrete requirement against a live product is a narrower task. If one agent does both jobs across many criteria, context grows too quickly and quality falls. Splitting those responsibilities reduces overload, improves repeatability, and makes failures easier to inspect. You can tune models, prompts, and tools differently for planning versus runtime verification rather than forcing one generalist workflow.

Should the verifier inspect the diff or the base branch?

Using the base branch can produce better verification because it keeps the agent from anchoring too tightly on the chosen implementation. A diff shows what changed, but it can bias the reviewer toward accepting the approach already taken. The base branch helps the verifier reason from product intent first, then evaluate whether the proposed solution makes sense in the broader system. The diff is still useful as supporting context, but relying on it alone can reduce critical distance and lead to weaker verdicts.

How many requirements should one verifier handle at a time?

As a default, one verifier should handle one requirement or one tightly related cluster of checks. The point is to keep context focused and prevent reasoning from one criterion from contaminating the next. When one long-lived agent reviews ten or fifteen requirements sequentially, earlier context becomes baggage. That increases the chance of skipped checks and inconsistent verdicts. Small, isolated verifiers are easier to parallelize, easier to debug, and usually more reliable than one large sequential verification session.

How do you prevent agents from inventing requirements that were never specified?

The best defense is grounding. Specs alone can leave too much room for inference, especially when they are vague. Combining the spec with relevant codebase context, runtime state, and scoped review instructions reduces the agent’s tendency to hallucinate extra obligations. It also helps to constrain the domain, such as front-end only versus back-end only. If the agent understands both intent and implementation reality, it is less likely to produce speculative findings about backward compatibility, architecture, or features that were never part of the task.

When should you use smaller models versus heavier models in the pipeline?

Use heavier models for ambiguous planning tasks and smaller models for narrow execution tasks. Requirement extraction is usually the harder reasoning problem because specs vary wildly in clarity and structure. Runtime verification of one concrete item, such as checking a button position or a click flow, can often be handled by lighter models with the right tools. This division keeps cost under control without degrading the parts of the pipeline that genuinely need stronger reasoning. Model choice should follow task complexity, not a one-model-for-everything rule.

What should you verify on every PR versus only on high-risk changes?

On every PR, verify the acceptance criteria for the feature being changed and a small set of revenue-critical or trust-critical flows that often regress, such as signup, subscription, authentication, or a key integration. Broader exploratory coverage should be reserved for high-risk changes or scheduled checks because full product exploration is expensive. The practical goal is to protect the paths whose failure creates the most customer pain or business loss. Coverage should be risk-weighted rather than uniformly exhaustive.

Why does sandbox isolation matter for preview-environment verification?

Preview-environment verification often requires agents to open unknown URLs and execute browser-based workflows against systems outside your core trust boundary. Doing that from the same environment that holds internal credentials, storage, or customer data creates an unnecessary attack surface. Isolated, ephemeral sandboxes reduce blast radius by limiting permissions and lifetime. Even if a preview environment behaves unexpectedly, the verifier only exposes a short-lived, narrow runtime. That separation is especially important as verification volume increases and agents interact with more external targets.

Key Takeaways

What strong agent verification actually requires

Better prompting helps, but architecture matters more.

● Shift in bottleneck: Agentic coding moves pressure from implementation to verification, especially when PR volume rises faster than reviewer trust.

● Architecture pattern: Separate requirement extraction from verification, then fan out checks across sub-agents rather than using one long-lived verifier.

● Grounding principle: Specs without code drift away from reality, while code without specs misses intent. The combination is what produces useful verdicts.

● Runtime focus: Many important failures only appear in preview or staging, so real browser-based verification is often necessary.

● Security boundary: Unknown preview URLs should be checked in isolated, ephemeral sandboxes, not from trusted internal infrastructure.

● Coverage strategy: Focus expensive agent verification on feature acceptance criteria and the few critical flows that cannot afford to break.

The teams that benefit most from coding agents are usually the teams that take verification design seriously.