AI-first development shifts the constraint from coding to review

AI-first development shifts the constraint from coding to judgment

Faster code generation can leave an engineering team with more work than it can safely approve. For leaders under pressure to deliver faster, the question is whether developers can specify, understand, and verify work as quickly as AI agents produce it. AI-first development requires teams to match delegated execution to their capacity to review and correct what reaches users.

Delegation can span the delivery process, from requirements gathering, architecture, and design through coding, testing, deployment, and maintenance. Developers supervise agents while taking greater responsibility for instructions, code comprehension, and assessment. Alongside this change in production, teams face a separate product decision: whether the application itself should contain AI components or support AI agents as users.

Bob Brauer, founder and CEO of Interzoid, a consultancy providing data-enrichment and structured-intelligence services, sees broader AI adoption as a response to stronger competition and demand for faster, more sophisticated, easier-to-use applications. Interzoid has a commercial interest in that adoption because it can increase demand for the company’s data and intelligence services. Brauer argues that using AI throughout delivery can make teams more effective and organizations more valuable. “Organizations that embrace AI-first development will be better positioned to deliver higher-quality software faster and gain a meaningful competitive advantage,” he says.

Faster generation brings execution and discovery forward

Brauer’s expected gains take shape in engineering work that teams already understand but struggle to complete. Platform migrations and updates to aging libraries compete with product development for limited attention, so accelerating them can change how much work a team finishes. “Work that used to stall in the backlog for quarters, like platform migrations and updating aging libraries, now clears in days,” says Collin Hogue-Spears, senior director, DTE, at application-security software company Black Duck Software. His company has a commercial interest in demand for software-security tools as teams increase their development activity.

Hogue-Spears also describes the benefit of finding problems earlier: “Defects surface at design time rather than in production, where a catch costs a code review instead of an incident.” Finding a defect during design lets developers revise the work before users depend on it. Once it reaches production, the team must investigate and respond while the application is running, so the timing of discovery affects the effort required.

Early discovery can also change product planning, according to Bob Hutchins, CEO of consulting and advisory firm Human Voice Media. His firm stands to benefit from organizations seeking advice on changes such as AI adoption, giving him a commercial interest in the transition he recommends. Hutchins says applying AI at the start of a project can expose conceptual problems in hours rather than weeks. “I developed full production applications where the first working prototype was completed prior to the kickoff meeting for a traditional shop,” he says.

A working prototype gives people something concrete to assess while a conventional process may still be preparing to begin. During implementation, William Flaiz, founder of data-cleansing products provider CleanSmartLabs, points to Claude Code as a tool that can produce clean code fast enough to move the delivery constraint elsewhere. As a software supplier, CleanSmartLabs has a commercial stake in development teams’ technology spending, although Flaiz’s example concerns another company’s tool. His point raises the next operational question: can the people responsible for delivery validate the growing volume of work?

Verification and explicit context determine whether speed survives delivery

Validation starts with the information available to the agent, which may be less complete than the knowledge experienced developers use. People often compensate for undocumented rules, difficult legacy code, and gaps in established processes through memory or manual intervention. An agent following written instructions lacks that informal context. The team must therefore make the relevant knowledge explicit before it can reliably delegate the same work.

Missing context also makes precise instructions harder to write, increasing the risk of inaccurate output. Developers then have to inspect, explain, and correct what the agent produces, adding work after generation. When correction consumes the effort saved earlier, a fast first result does little to improve completed delivery. The useful measure is how much effort remains after review and correction.

Because instructions shape the result, Hutchins argues that AI-first work pushes teams toward clearer documentation. The specification becomes the directions the AI follows, so writing it requires people to state what the system should do, which constraints apply, and what counts as an acceptable outcome. Teams that rely on unwritten agreement must settle those questions before handing work to an agent. Documentation becomes part of execution.

Clear specifications still need a review process capable of assessing the output. Weak quality assurance, security practices, testing processes, or relevant skills leave a team poorly equipped to judge whether generated work meets its requirements. More generation can then lengthen the queue awaiting assessment. Where delivery also depends on manual intervention, the team must account for that work as agent activity expands.

Hutchins recommends establishing review before building agent pipelines, the linked stages through which agents carry out work. His warning is direct: “AI will generate ‘plausible’ garbage without hesitation.” Because output can look reasonable while containing serious errors, a quick inspection may leave substantive correction undone. The review process must define who checks the result and what they need to establish before it proceeds.

The cost of skipping that assessment extends beyond engineering. Hutchins points to consultants who returned client payments after delivering fabricated citations, describing the damage as a process failure caused by generation expanding faster than verification. The example concerns generated material generally, but the responsibility also applies to delegated engineering: the organization remains accountable for checking what it delivers. A convincing presentation cannot establish fitness for use.

To put that responsibility into practice, Hutchins tells teams to identify the human decision within each workflow. “Design for the human checkpoint,” he says, asking where someone with appropriate judgment must review work before it is sent or released. Review must remain active as work progresses so unresolved issues reach someone able to address them. An approval point needs both an accountable person and a clear decision for that person to make.

AI changes both software production and product architecture

Human review governs delegated development, while AI-enabled products require further decisions about the software’s own behavior. A team can use an agent to build conventional software, changing its production process. When the product contains AI components or serves agent users, its architecture must also govern their actions, permissions, and possible errors. The team is then designing both how the software is built and how AI participates in its operation.

Planning for that participation early can avoid difficult changes later, Hutchins says. He describes adding AI to an existing project as often costly and complicated, while designing for it from the outset can produce clearer data movement, more explicit permissions, and interfaces suited to agent interaction. The distinction is in the scope of the product change: adding a chatbot preserves much of an existing application, whereas designing around AI can change what users and agents can accomplish. “Designing for AI provides an entirely new type of product,” he says.

Architectural preparation is also central to Mona Rajhans’s argument. Rajhans, senior software engineering manager at cybersecurity-products company Palo Alto Networks, leads a 14-person AI engineering team; her employer has a commercial interest in demand for security as organizations adopt AI systems. “AI-first development matters because the alternative is increasingly untenable,” she says. She warns that introducing AI into an architecture unsuited to it can cause serious failures because the design may lack explicit conditions for safe agent action.

Those conditions begin with decision authority. “The job is less about writing code and more about designing the boundaries, where the agent acts, where the human decides, and what happens when the agent is wrong. That last question is the one most teams skip until it is too late,” Rajhans says. The architecture must establish what the agent is allowed to do and how the system responds to an incorrect action. Human approval is one control within that design, alongside the application’s handling of an unacceptable result.

Handling such results requires teams to account for nondeterminism, the possibility that repeated use produces different behavior. Rajhans says developers must become comfortable with this property of AI systems: “AI-first systems do not behave the same way twice. Testing, observability, and failure handling all have to be redesigned from scratch around that reality. Teams that treat AI outputs like function returns get burned fast.” Her warning concerns systems whose AI behavior can vary during operation.

That variation means a successful run cannot establish how every later run will behave. Testing must cover variation, while observability, the ability to understand system behavior through information recorded during operation, must help the team recognize problematic outcomes. Failure handling must then determine what happens after an unacceptable result. These design and operating responsibilities continue after developers finish reviewing generated code.

Even when the product’s behavior is conventional, faster implementation leaves architectural decisions for people to make. Flaiz argues that senior architects bring the experience needed to decide what to build, how to organize it, and where it could fail. Bypassing that judgment, he warns, can quickly produce software that later breaks under user load. The team remains responsible for deciding whether the overall system can support real use.

Real use also requires knowledge of people, so Flaiz recommends involving user-experience expertise throughout development. “AI can write requirements, specs, and assist with architecture, but it doesn’t understand your users. It doesn’t know the habits of how real users interact with software. That comes from watching [user experience] tests, analyzing behavior, and being human.” Observed user tests and behavioral analysis give the team evidence about how people interact with the product. Keeping that expertise involved lets actual behavior shape decisions as development proceeds.

Engineering roles shift toward specification, architecture, and orchestration

These design responsibilities change the capabilities organizations need from their developers. Rajhans says the architect’s role is changing faster than many organizations have recognized, creating a training need for managers as well as engineers. Teams must identify the skills needed for AI-led delivery and adjust responsibilities accordingly. Supervising delegated execution becomes part of engineering work.

Supervision depends on understanding what agents produce, which makes rapid code comprehension especially valuable. Hogue-Spears argues that reading code quickly and assessing it accurately becomes a scarce skill as agents take on more implementation. “The strongest developers will work as architects: they frame the problem, direct the agent, and verify the result. That is how one engineer safely oversees the work of several.” The capacity he describes depends on the engineer completing all three tasks.

This division becomes explicit in agentic workflows, where agents use reasoning, planning, and external tools to pursue complex goals under human supervision. People specify the outcome, define the system, consider unusual scenarios, and assess the resulting work. Hutchins describes the central change as a move from entering code manually toward specifying what should be accomplished. Architectural judgment and the ability to assess and revise output therefore become more valuable.

Specification also includes product decisions, broadening the work beyond technical instructions. Brauer expects developers to spend more time defining goals, weighing product constraints, revising architecture, designing understandable interfaces, planning data movement, and estimating expected outcomes. Agents can assist with code, test plans, and documentation, as well as deployment scripts and monitoring for maintenance needs. Developers remain responsible for deciding what those activities should achieve and whether the results fit the product.

The growing importance of those decisions underlies Flaiz’s staffing proposal: concentrate on senior architectural judgment while cutting or redesigning traditional entry-level coding positions. His proposal puts pressure on roles built around manually producing implementation that agents can generate quickly. It also raises a practical question for organizations that need to develop future senior engineers. Junior developers need a route into the responsibilities that remain valuable.

Flaiz offers agent orchestration as one such route. Orchestration means coordinating agents: deciding where one hands work to another, recognizing conflicts between their outputs, and establishing which result takes precedence. Those decisions require product context and an understanding of users, so entry-level developers need training to perform them. Moving into orchestration changes the work they must learn and the judgment they must exercise.

Preparing people for that work also changes the manager’s role. Engineers need to understand what they are asking agents to do, how to coordinate the resulting activity, and how to assess it objectively. Managers need enough understanding of the changed roles to assign responsibility and provide relevant development opportunities. Staffing decisions must account for which people can acquire those capabilities alongside which tasks agents can perform.

Scale only after measuring time saved against corrective work

The need to develop those capabilities makes a limited rollout a practical starting point. “Begin small and track everything,” Hutchins says, recommending approved tools and clearly defined usage rules. A monitored rollout lets developers use AI while the organization observes where it saves effort and where it adds correction. It also lets the team exercise its review process before increasing the amount of delegated work.

The rollout’s measurements should guide expansion. Leaders need to compare developer time saved with time spent on inspection and rework, then use the result to decide where adoption should grow. A bounded experiment can meet pressure to move quickly while making those costs visible. Expansion becomes defensible when reviewed and corrected work reaches delivery with less total human effort.

Key takeaways for decision-makers

• Review capacity sets the pace: Engineering managers need to match AI-generated work to developers’ capacity to understand and verify it. Faster implementation improves delivery when review and correction keep pace.
• Clear specifications support reliable delegation: Engineering teams need to document requirements, constraints, and knowledge held informally by experienced developers. Defined human checkpoints give reviewers responsibility for deciding whether agent output can proceed.
• AI-enabled products need explicit boundaries: Architects need to define where agents act, where people decide, and how systems handle incorrect results. Variable AI behavior also requires testing, observability, and failure handling designed around that variability.
• Engineering roles shift toward judgment: Engineering managers need to develop skills in specification, architecture, code comprehension, and agent orchestration. Junior developers need training and a route into these responsibilities as agents take on more implementation.
• Scale adoption against total effort: Engineering managers can use bounded rollouts with approved tools and clear usage rules to measure time saved against inspection and rework. Expansion is justified when reviewed, corrected work reaches delivery with less total human effort.