# What Developers Should Do While an AI Agent Writes the Code

Introduction: When Writing Code Is No Longer the Scarce Skill

Imagine handing an AI coding agent a design document and asking it to implement a data processing pipeline that ingests, cleans, transforms, and stores data. You hit Enter, and the agent begins: reading the document, breaking down the tasks, generating code, and writing it out file by file. The time that follows is what we might call the “gap.”

Intuitively, the gap looks like time spent waiting. In mature engineering practice, though, it is one of the most valuable thinking windows a developer has. Once the act of coding is largely automated, what defines your value is no longer how many lines of code you write, but whether you can define the problem clearly and vouch for the result. Here are seven areas where you should focus during the gap.

1. Revisit Architectural Boundaries and Design Soundness

Coding agents are good at filling in implementation details within an existing framework, but they won’t stop to question whether the architecture itself is sound. The first thing to do during the gap is to step out of the code and take a design-level, “god’s-eye” view of the whole picture.

Take the data processing pipeline as an example. Ask yourself:

• Does each processing stage have a single, well-defined responsibility, and is coupling under control?
• Are the data format contracts between pipeline stages clearly specified?
• Are non-functional requirements such as failure retries, idempotent processing, and backpressure control addressed in the design?

If the pipeline would overwhelm downstream storage when data volume spikes, then the overall solution is fragile, no matter how elegant each piece of generated code looks on its own.

Work through these boundary questions while the agent is coding. Then, once it delivers, you can quickly tell which parts align with the intended architecture and which are directional deviations that need to be reworked.

2. Feed Context Carefully and Clarify Requirements

The quality of an agent’s output depends heavily on the quality of the context you give it. The gap should not be idle waiting. Instead, use it to keep refining your inputs:

• Fill in the constraints the design document leaves vague, such as data volume, latency requirements, and exception-handling strategies.
• Define interface contracts and data structures precisely.
• Provide a typical input/output example so the agent can align with your expectations.
• Outline the intent behind a set of edge-case tests. Tell the agent in advance how to handle empty records, dirty data, and excessively long fields, for example.

Context is not a case of “the more, the better.” It needs to be precise. Redundant or even contradictory information can actually reduce how accurately the agent understands the task. Treat context management as an engineering activity in its own right. It is one of the highest-return things you can do during the gap.

// Context for an agent should include clear contracts, not vague natural language
inputSchema:  { rawRecords: []byte, source: string, batchId: string }
outputSchema: { cleaned: []Record, rejected: []Record, metrics: Metrics }
constraints:  [ no more than N records per batch, timeout of T seconds, 3 retries on failure, must be idempotent ]

3. Design the Test Strategy and Quality Gates

Agents can generate tests, but they lack the business judgment to know which tests truly matter. The gap is the right time to design your test pyramid and acceptance criteria. Decide which of the following apply:

• Core transformation logic that needs unit test coverage
• External dependencies such as message queues, object storage, and databases that require integration testing
• Scenarios that must be covered by a regression safety net

Capture these decisions as acceptance criteria, ideally ones that are measurable. That way, you can later check automatically whether the agent’s output meets the bar.

Quality gates should also cover engineering conventions such as static analysis, coding standards, and coverage thresholds. Once these rules are defined upfront, you won’t have to manually re-check every piece of output. The pipeline will hold the line for you.

4. Shift Code Review from “Reading the Implementation” to “Verifying the Intent”

When the agent submits its code, the gap enters its verification phase. The biggest mistake at this stage is to go through it line by line checking whether it is written correctly. Line-level correctness is exactly what agents tend to handle well. What really deserves your review is whether the intent is correct.

Consider the data processing pipeline again. Review should focus on questions such as:

• Does each transformation step follow the business rules?
• Is the handling of exception branches consistent with the design?
• Are there hidden side effects or resource leaks?
• Have boundary conditions, such as empty sets, extreme values, and concurrent writes, been accounted for?

By moving your review lens from syntax and style to logic and intent, you can catch the truly critical issues within the time you have.

5. Backstop Security, Compliance, and Maintainability

Code generated by an agent may come with unsafe defaults, such as hardcoded credentials, queries built by string concatenation, overly permissive access rules, and sensitive data written to logs in plaintext. These are exactly the high-risk points you should actively hunt for during the gap. Check whether:

• Any secrets or personal information end up in the code or logs
• Data access follows the principle of least privilege
• External input is properly validated and escaped
• Any newly introduced dependencies are trustworthy

These security baselines cannot rely entirely on the tools’ own discipline. The developer has to be the last line of defense.

Maintainability also belongs in this backstop category: are names self-explanatory, is the code organized into sensible modules, are key paths documented with comments where necessary, and is configuration kept outside the code? Together, these factors determine whether anyone can still understand and evolve the pipeline three months from now.

6. Orchestrate the Engineering Workflow and Connect It to Automation

The gap is also a good time to refine your engineering pipeline. How will the agent’s output flow smoothly into automated build, test, and deployment? Use the time to make sure the essentials are in place, including:

• Continuous integration trigger conditions
• Environment configuration
• Artifact management

On the cloud, this usually maps to a set of common capabilities: code hosting, pipeline orchestration, containerized runtimes, logging, and monitoring with alerting. Platforms such as Baidu AI Cloud offer this type of general-purpose engineering infrastructure. It connects your written output with automated quality checks. If you sort out this orchestration during the gap, every delivery from the agent gives you a trustworthy, observable feedback loop, instead of leaving you to sense the state of the code by hand.

7. Capture Knowledge and Strengthen Team Collaboration

Every round of human–AI collaboration should produce assets the whole team can reuse. These include the implementation approaches the agent works out, the context templates you refine, how your acceptance criteria evolve over time, and the pitfalls you hit along with ways to avoid them. All of it is worth recording and archiving.

Spending the gap on this “invisible work” pays off enormously in the long run. It lowers the cost of repeated trial and error across the team, and it lets similar requirements be implemented correctly and faster next time. A developer’s shift from “writing code” to “distilling methodology,” and a team’s shift from “coding at scale” to “delivering with quality,” happen precisely through these quiet, incremental efforts.

Conclusion: Redefining the Developer’s Core Competitiveness

Back to our opening question: what are you doing during the gap while the agent writes code? The answer is neither “waiting” nor “worrying about being replaced.” It is moving yourself into the high-value work that agents cannot yet handle independently:

• judging architecture
• defining requirements
• designing tests
• reviewing intent
• upholding security
• orchestrating engineering
• capturing knowledge

Coding itself is becoming a foundational skill that automation amplifies. Knowing what to write, why to write it that way, and how to prove it is correct is the hard-to-replace core competitiveness. Developers who make the most of the gap are not the ones agents replace. They are the ones who turn agents into a force multiplier.