If a coding agent writes the implementation, is the developer’s new job simply to review it? That description misses much of the work that makes software useful. Someone still needs to decide what to ask for, what must remain unchanged, how to test the result, and whether the change belongs in the system at all.
Code review remains important. It is one activity inside a larger engineering process. Moving generation to AI does not make that process disappear, and it does not make every engineer interchangeable with somebody who can approve a diff.
Specification becomes more consequential
Consider a request to “add customer discounts.” An implementation can appear quickly while basic questions remain unanswered. Does the discount apply before tax? Can it combine with a negotiated contract price? Who may approve an exception? Does changing it affect an invoice already issued?
The engineer’s contribution starts before code generation: expose the ambiguities, work through them with the business, and translate the agreed rule into a model the system can enforce. An agent can help enumerate cases. It cannot independently decide which commercial obligation the company intends to accept.
This also changes what a useful task looks like. A bounded change with explicit interfaces and acceptance criteria is easier to delegate and verify than a broad instruction to “improve the customer experience.” Breaking down work is not merely administrative overhead; it controls how much uncertainty enters each generated change.
Someone must understand the system beyond the prompt
A local change can be correct in isolation and wrong in context. Updating an order status may affect invoicing, warehouse work, customer notifications, and a downstream report. The agent may not have seen all those dependencies, particularly when they cross repositories or belong to external systems.
Engineers need a working model of those relationships. They decide where a rule belongs, which contracts must remain stable, and how a failure should propagate. They also need to recognize when the requested feature would create unnecessary coupling or duplicate an existing capability.
The practical response is not to put the entire company into every prompt. Maintain clear boundaries, accessible interface documentation, and short records of important decisions. Then check that generated changes follow them. Documentation is useful context; tests and enforced controls provide different kinds of evidence.
Verification needs an independent basis
Asking an agent to write code and then asking the same agent whether the code is correct can produce a persuasive explanation of the same mistaken assumption. It is useful feedback, but not a sufficient review process.
Define expected outcomes separately. For the discount feature, derive tests from the agreed pricing rules, not from the generated function’s current output. Include cases with existing contracts, rounding boundaries, denied approvals, repeated requests, and changes to previously recorded transactions.
A human reviewer should inspect the scope of the change as well as its style. Did a helper weaken validation elsewhere? Did the agent remove a failing test rather than fix the defect? Does a new dependency introduce operational obligations? Faster generation can increase the amount of material waiting for review, so smaller changes become more valuable.
Measure outcomes rather than typing speed
Lines of code and completed prompts are poor measures of engineering value. Useful measures include the time to deliver an accepted change, review effort, escaped defects, rework, and the team’s ability to maintain the result. No single metric captures all of that.
Compare like-for-like work where possible, and account for differences between familiar repositories, new prototypes, and complex integration tasks. A tool that helps one person build a first version may have a different effect when another person must safely modify a mature system.
METR’s 2026 update on its developer-productivity experiments is instructive because it explains uncertainty and selection effects rather than offering a universal productivity multiplier. Research findings need their context; neither a favorable benchmark nor an unfavorable one establishes what your team will experience.
Organize around responsibility, not a new job title
Teams do not necessarily need a separate “AI supervisor” for every project. They need to decide who owns requirements, architecture, implementation review, release approval, and production operation. Existing engineers may cover those responsibilities with different tools and different allocations of time.
Less experienced developers still need opportunities to understand systems and develop judgment. Assigning them only to accept generated changes without guidance can conceal gaps rather than build capability. Pairing, focused reviews, and asking someone to explain why a test is meaningful remain useful practices.
Likewise, senior engineers should not become an unlimited approval queue for changes they have no time to understand. Limit work in progress, make review expectations explicit, and reserve specialist attention for decisions with substantial consequences.
The changing role is broader than moving from author to editor. Engineers become responsible for shaping a reliable process in which generation is one step. They can delegate more implementation while remaining accountable for what the application does, why it does it, and what happens when it fails.
Related reading
- AI-Assisted Software Development: What Engineers Still Need to Own
- From AI Prototype to Production: What to Keep, Refactor, or Rebuild
- Vibe coding: platforms, B2B risks, and delivery choices
Put the right guardrails in place
SaM Solutions can assess your project, review its architecture, and help decide where AI-assisted development fits.