Agentic Development

Executable Specifications for Scaling Agentic Coding

Scaling agentic coding starts with executable specifications, not just giving more engineers access to a coding agent. Teams need agent guardrails, local test-suites, and scoped context injection so every generated change meets the same standard a human reviewer would expect.

MMichael SwindellSeptember 13, 202615 min read

Agentic coding at scale is not simply giving more engineers access to a coding agent. It is the disciplined rollout of agent-assisted development across teams with reliable delivery practices, shared standards, measurable workflows, and human support. Small pilots can hide weak CI/CD, unclear ownership, missing tests, and inconsistent review expectations. At organizational scale, those gaps become the constraints that determine whether agents accelerate delivery or multiply rework. An agent-first control plane such as Levr can keep issues, acceptance criteria, test results, and human approvals connected to the work. The goal is sustainable adoption: better software flow, reliable quality signals, and careers that evolve with changing engineering work.

At a Glance

Scaling coding agents requires operational discipline, not just licenses and training.

Start with delivery health: CI/CD, test automation, coding standards, and a usable engineering platform are prerequisites for safe acceleration.
Grow adoption socially: Communities of practice spread proven workflows more effectively than isolated tool rollouts.
Expect uneven results: Agentic coding can widen the performance gap between engineers who learn effective agent workflows and those who do not.
Measure bottlenecks end to end: Faster code generation can expose slow reviews, weak testing, unreliable deployment, and unclear ownership.
Design for people: Sustainable on-call, meaningful career paths, and psychological safety remain essential during workflow change.

What foundations should be in place before expanding coding-agent use?

Before expanding agentic coding, teams need dependable CI/CD, executable tests, clear coding standards, and a delivery platform that can safely move changes from development through production. Agents amplify existing system behavior, including existing weaknesses.

The 2025 DORA report frames this as a systems problem. Improving one part of an interconnected delivery system can expose bottlenecks elsewhere. If code generation speeds up while testing, deployment, or operational feedback remains weak, the organization may create more work without increasing meaningful throughput.

CI/CD must provide a reliable path to production

Agents can generate changes quickly, but code has little value if teams cannot build, validate, and release it predictably. A healthy CI/CD pipeline gives agent-created changes a repeatable route through compilation, automated checks, deployment controls, and production feedback.

Look for signs that the path is already unreliable:

Builds are frequently flaky or slow enough that engineers avoid running them.
Deployments depend on manual steps known only by a few people.
Rollbacks are unclear, risky, or rarely rehearsed.
Production failures do not reliably feed back into backlog and engineering priorities.

Tests must be runnable, trusted, and connected to change

Agents need feedback. If an agent cannot run relevant tests, it has no dependable way to learn whether its implementation broke behavior. More importantly, humans reviewing the change also lose a critical signal.

Test quantity alone is not enough. Teams need tests that represent meaningful behavior, execute reliably, and are easy to run within normal development workflows. A suite full of flaky checks or stale assertions will not create confidence, regardless of whether a human or coding agent wrote the change.

Coding standards need to be explicit

An agent cannot infer a stable definition of good engineering work when the humans on a team do not agree on it. Standards should cover the practices that materially affect maintainability and safety, such as architecture boundaries, testing expectations, naming, dependency rules, observability, and documentation.

These standards do not need to become a heavy handbook. They need to be discoverable, current, and reinforced through automated checks where possible. The best instructions are those that agents and engineers can validate rather than debate after implementation.

Why is scaling agentic coding different from running a small pilot?

A small coding-agent pilot tests whether a tool can help an individual developer perform a task. Scaling agentic coding tests whether the surrounding delivery infrastructure can handle a higher frequency of machine-generated changes. In a pilot, a developer often masks technical gaps by manually repairing broken builds or navigating opaque service boundaries. These local fixes are invisible until you attempt to scale.

At scale, agents interact with complex repository structures, distributed service boundaries, and varying quality standards. The transition from pilot to scale is not about increasing the number of users; it is about shifting from ad-hoc prompting to consistent, integrated workflows. Developers must move from asking "Which agent should I use?" to "What input structure and validation gates are required to make this agentic task repeatable?" Success at scale requires a control plane that connects the coding agent—such as Claude Code, Cursor, Codex, or Copilot—to real-time project context, ensuring that every implementation is validated against the same technical constraints. This tracks with the 2025 Stack Overflow Developer Survey, where AI adoption kept climbing even as developer distrust in AI-generated output overtook trust for the first time.

Why can coding agents create a wider engineering performance gap?

Coding agents do not replace technical expertise; they amplify the ability to decompose and verify complex systems. An performance gap emerges because the efficiency of an agentic workflow is dependent on the quality of the developer's input and their ability to interpret the output. Developers who treat agents as black boxes often spend more time debugging faulty code than they would have spent writing it themselves, where the developer acts as an architect and verifier of agentic output, the same judgment call covered in when should AI agents require human approval article.

The high-performing developer in an agentic workflow is one who can define granular problem statements, provide relevant system context, and construct robust verification steps. The gap between a productive developer and an overwhelmed one is defined by technical proficiency in workflow design: identifying where a task can be delegated, knowing what metadata is required for the agent to succeed, and understanding how to inspect the generated code for architectural fit. This means the primary skill-set shifts from manual implementation to system oversight, where the developer acts as an architect and verifier of agentic output.

Give coding agents the same context as the engineering team.

Levr keeps issues, acceptance criteria, tests, workflow gates, and attributed activity in one shared control plane so agent work remains connected to the delivery process.

Explore agent-first project workflows

How to define executable specifications for agents at scale

To scale agentic coding, developers must replace ambiguous instructions with executable specifications that an agent can parse and act upon. This means defining tasks as sets of constraints rather than open-ended requests. A well-defined specification includes the expected interface, required test coverage, and clear boundaries for the logic to be generated. By using a control plane to attach these specifications directly to the work item, you ensure the agent consumes the same requirements as a human developer would. Instead of providing instructions in natural language that are prone to misinterpretation, translate criteria into structured schemas or documentation that the agent-first workflow can ingest. This turns the requirement into a guardrail, ensuring the agent aligns with team standards before it ever begins writing code.

How to build local test-suites that act as agent guardrails

An agent’s output is only as reliable as the feedback loop provided to it. To scale, you must build high-fidelity, local test-suites that provide immediate, actionable results during the agentic process. If an agent cannot execute tests, it effectively operates in a vacuum, leading to regressions that are only discovered during CI/CD, the exact gap covered in verification debt and how to verify AI-generated code. Developers should design modular, fast-running test-suites that specifically target the sub-systems the agent is modifying. By exposing these suites to the coding agent, you create an automated guardrail that validates correctness, behavior, and integration logic in real-time. This forces the agent to iterate on its own code until it meets the predefined pass criteria, allowing the developer to review verified, tested solutions rather than raw output.

This mirrors what VirtusLab's research on testing and evaluating agentic systems calls a 'verification gap' created by non-deterministic agents, which mandates trajectory-level evaluation rather than one-off checks.

How to structure context-injection so agents handle specific sub-systems effectively

Agents often struggle with "context overload" when exposed to entire repositories. To enable effective scaling, developers must implement targeted context-injection that limits an agent’s view to the specific sub-systems relevant to the task. This involves structuring codebases with clear domain boundaries, structuring codebases with clear domain boundaries, the same principle behind where agent context lives and using metadata to help the agent index only the necessary dependencies, interfaces, and architecture documentation. By isolating the agent’s reach, you reduce the probability of unintended side effects and halluncinated calls to unrelated APIs. When an agent is confined to a specific service boundary—provided with only the local scope and the relevant interface contracts—it can perform deeper, more accurate work on complex sub-systems. This disciplined approach to scoping ensures the agent functions as a surgical tool for specific features rather than a blunt instrument hitting the entire codebase.

How does a control plane make scaling agentic coding repeatable?

Executable specifications, test-suite guardrails, and scoped context injection each solve one part of the scaling problem, but they only compound when they live in the same place a team already works. A control plane attaches acceptance criteria and verification requirements to each issue, tracks which coding agent touched which change, and gates a merge until linked tests pass. That turns individual good habits into an organization-wide pattern instead of practices that live in one engineer's head.

How do you prevent faster code generation from becoming more rework?

Preventing rework requires treating generated code as one stage of delivery, not the finished product. Define intent, require automated verification, preserve human approval for meaningful risk, and measure where work waits after implementation is complete.

Code generation is often the easiest stage to accelerate. The constraint moves somewhere else, commonly into pull request review, test failures, integration conflicts, release approvals, incident response, or unclear product decisions. If no one measures those queues, teams may mistake increased activity for increased delivery.

Use a clear workflow contract

Every agent-assigned task should have enough context to support a good outcome. That includes a bounded problem statement, relevant acceptance criteria, technical constraints, expected test coverage, and a definition of done. The aim is not to over-specify every implementation detail. It is to remove avoidable ambiguity.

One way to structure this in Levr’s agentic workflow is to define intent in natural language, let an agent work against the associated issue and criteria, record automated verification, and retain a human review or approval gate where it matters.

Move quality checks closer to implementation

Mandatory review of every line of code may become difficult to sustain as agent-created output grows. That does not mean abandoning quality control. It means shifting some assurance earlier through stronger specifications, reliable automated tests, static analysis, security checks, and clear deployment gates.

Human review remains important for decisions that need product judgment, architecture context, risk assessment, or accountability. The review should focus on those high-value questions rather than becoming the only place where correctness is discovered.

Keep work, evidence, and ownership together

Disconnected tooling creates a familiar failure mode: the issue tracker says one thing, a coding-agent session contains another, test results live elsewhere, and a pull request has no clear record of why a decision was made. A shared control plane helps teams connect intent, implementation, test evidence, and approvals.

Levr supports this model by keeping issues, tests, runs, workflow gates, and activity history on the same project objects. That can make it easier to see whether a task is blocked by implementation, verification, or human decision-making rather than assuming the agent is the problem.

Specifications That Travel With the Work

The Levr Dashboard attaches acceptance criteria and verification requirements directly to each issue, so a coding agent and a reviewer see the exact same specification. Linked tests and workflow gates confirm the agent met those criteria before anything merges.

Agentic Software Development | Control Plane | Manual & Automatic Verification Issue Tracking |Levr

Levr Dashboard: executable specifications linked to issues and gates.

Technical Deep Dive FAQ

What is agentic coding?

Agentic coding is software development in which a coding agent can do more than autocomplete code. Depending on the environment and permissions, it can inspect a repository, plan changes, edit files, run commands, execute tests, and report results. The developer remains responsible for deciding what work should happen, providing sufficient context, reviewing important decisions, and validating the outcome. Agentic coding is most useful when it operates inside clear technical and delivery boundaries.

What does agentic coding at scale mean?

Agentic coding at scale means introducing coding-agent workflows across multiple teams, repositories, and delivery environments while preserving quality, operational safety, and organizational clarity. It includes access to tools, but also training, standards, workflow design, measurable outcomes, and support for engineers adapting to new ways of working. A successful rollout does not only increase usage. It makes effective practices repeatable and keeps generated changes connected to testing, deployment, and accountability.

Do coding agents make weak engineering teams more productive?

Not necessarily. Coding agents can accelerate implementation, but they also expose weaknesses in the delivery system. If a team lacks reliable CI/CD, maintainable tests, clear coding standards, or a dependable route to production, more generated code can create more review load, defects, and deployment friction. Improve delivery fundamentals first, then introduce agents into bounded workflows where quality signals and ownership are already clear.

What should a team assess before adopting coding agents?

Start with a delivery discovery process. Assess build reliability, deployment frequency and safety, automated test coverage and trustworthiness, coding standards, platform support, repository access, security controls, and the health of the on-call rotation. Also assess where work currently waits, such as in review queues, integration testing, approval processes, or release coordination. These constraints will determine whether coding agents accelerate the system or simply move the bottleneck.

How should teams measure coding-agent adoption?

Usage metrics such as weekly active users can show whether engineers are trying the tools, but they cannot prove business or engineering value on their own. Pair adoption data with delivery and quality measures, including lead time, change failure patterns, test health, blocked work, incident impact, and developer feedback. Qualitative evidence matters too. Track which workflows succeed, where output is unreliable, and what guidance helps teams repeat good results.

How can teams reduce low-quality agent output?

Reduce low-quality output by giving the agent a bounded task, relevant repository context, explicit acceptance criteria, coding standards, and executable tests. Ask it to inspect and explain before making broad changes when the problem is unfamiliar. Keep permissions proportional to risk, and require automated validation before a change moves forward. If output remains weak, investigate missing context or unclear expectations before assuming that different prompting alone will solve the problem.

Should coding agents be allowed to merge pull requests automatically?

Automatic merging should depend on the risk and maturity of the workflow, not on a blanket rule. Low-risk, well-bounded changes with reliable checks may support more automation. Changes involving security-sensitive systems, production infrastructure, architecture decisions, or unclear requirements should retain stronger human approval. The key is to define explicit gates based on test evidence, policy checks, and ownership, then apply those gates consistently whether a human or coding agent authored the change.

How do shared project context and MCP help coding agents?

Model Context Protocol, or MCP, gives coding agents a standard way to interact with tools and external context. Shared project context makes tasks more actionable by connecting an agent to current issues, acceptance criteria, test status, workflow state, and prior decisions. This reduces reliance on a single disconnected prompt. In an agent-first platform, agents and humans can work from the same live project objects while the team retains an attributed history of activity.

Which engineering skills matter most in an agentic workflow?

Strong agentic workflows reward systems thinking, specification writing, debugging, verification, architecture judgment, and the ability to break ambiguous work into testable pieces. Engineers also need enough domain knowledge to recognize incorrect output and understand downstream effects. Prompting can help, but it is not the core skill. The durable advantage comes from knowing how to define useful work, delegate it safely, inspect the evidence, and make sound technical decisions when automation cannot.

How can engineering managers support people through this change?

Engineering managers should create room for experimentation, share successful workflows across teams, and treat failed experiments as useful evidence rather than individual shortcomings. They should also protect sustainable on-call expectations, clarify how roles are evolving, and invest in development plans that reflect new technical needs. Avoid reducing adoption to a usage target. The stronger goal is to help engineers build confidence, preserve accountability, and spend more time on valuable work.

Key Takeaways

Scaling agentic coding is a delivery-system redesign, not a simple expansion of coding-agent access.

The teams that benefit most pair agent speed with clear engineering constraints.

Fix the system first: Reliable CI/CD, tests, standards, and platform capabilities give coding agents useful feedback and safe boundaries.
Build adoption through practice: Communities that share specific workflows turn isolated success into organizational learning.
Measure the full flow: Track where work waits after code generation, especially verification, review, deployment, and operational response.
Keep humans central: Product judgment, reliability ownership, architecture choices, and career development matter more, not less.

Agentic coding works best when teams use it to improve the entire software delivery system, not merely to produce more code.