Agentic Development

AI coding agents: how developers build the judgment to supervise them

AI coding agents can now handle much of the syntax, scaffolding, and routine implementation work that once filled a developer's day. That shifts the job toward supervising agents: specifying work clearly, reviewing evidence, and making sound release calls. Here is how developers build that judgment.

MMichael SwindellSeptember 6, 202610 min read

AI coding agents are fundamentally changing the engineering workflow, shifting the primary value of a developer from manual implementation to high-level oversight. While coding agents can handle much of the syntax, scaffolding, and routine implementation that once occupied a developer's time, developers still have to decide what gets built, verify it, and own the result. The capability that differentiates modern engineering is the ability to connect product intent, system design, delivery constraints, and agent supervision. Success in agentic coding requires learning to specify work clearly, supervise agents, investigate failures, and make sound release decisions. An agent-first control plane such as Levr gives teams shared project context, explicit quality gates, tests, and accountable handoffs while developers build the earned judgment that automation cannot provide.

At a Glance

AI coding agents change the nature of engineering, making developer judgment more critical than ever.

Coding is no longer the whole job: Coding agents can compress routine implementation, but developers must frame, verify, and own the work.
Experience remains scarce: Production incidents, ambiguous requirements, and operational tradeoffs create a form of judgment that cannot be downloaded from documentation.
Gates matter more than prompts: Reliable coding agent workflows depend on clear definitions of done, tests, review boundaries, and escalation rules.
Autonomy should be selective: Low-risk internal work can run with more freedom than customer-facing or business-critical systems.
Developer roles are evolving: Supervision, investigation, specification, and verification skills are becoming core parts of the engineering path.

Why should developers focus on earned versus teachable knowledge?

Teachable knowledge can be explained, documented, searched, or generated on demand. Earned knowledge develops through context, consequences, and repetition. Coding agents can make teachable knowledge easier to access, but they cannot fully substitute for the practical judgment engineers acquire by operating and repairing real software systems.

Examples of teachable knowledge include language syntax, framework conventions, API usage, standard algorithms, and common debugging techniques. A coding agent can help a developer access these quickly, often with explanations tailored to the codebase at hand.

Earned knowledge is different. It includes recognizing an unsafe deployment pattern, estimating whether a system can tolerate a specific load profile, anticipating a failure mode during an outage, and knowing which shortcut will create expensive maintenance later. These insights come from seeing systems succeed and fail under conditions that textbooks cannot reproduce completely.

Teams need a deliberate way to expose developers to production behavior, design reviews, customer context, post-incident analysis, and release decisions. Artificial drills can help, much like fire drills create practice for rare emergencies, but they should complement real supervised responsibility rather than replace it.

How does a developer's role change with AI coding agents?

The role of a developer in an agentic coding environment shifts from completing isolated coding tickets toward learning how to define work, direct coding agents, evaluate evidence, and escalate uncertainty. The goal is not passive prompt use; it is active technical judgment built through supervised ownership of increasingly complex engineering outcomes.

Developers should still learn to read code and reason about systems. The difference is that they do not need to spend every hour manually producing boilerplate before they can contribute. They can ask an agent to explain a module, map a call path, propose tests, or identify relevant files before bringing focused questions to the team. This creates an effective supervision loop:

Understand the intent: Read the issue, acceptance criteria, relevant architecture, and constraints before asking an agent to act.
Use agents for exploration: Ask for explanations, hypotheses, test ideas, and implementation options rather than accepting the first patch.
Inspect the evidence: Review diffs, test results, failure logs, and behavior in a realistic environment.
Escalate with context: Provide the team with the attempted approach, observed result, remaining uncertainty, and recommended next step.
Capture the lesson: Turn decisions and recurring failures into reusable team knowledge.

Team structure changes too, as covered in reinventing the dev team for agentic software.

Give agents context, constraints, and proof requirements.

Shared issues, acceptance criteria, tests, workflow gates, and attributed activity make it easier for developers to supervise agent work without losing the project context.

Explore Levr for agent-first engineering workflows

Where should humans remain in the loop for coding agent workflows?

Humans should remain responsible for defining intent, approving meaningful changes, and deciding whether evidence meets the release bar. Teams can automate more execution between those boundaries, but autonomy should depend on system criticality, blast radius, and the cost of an undetected failure.

A coding agent can generate patches, run tests, create pull requests, and investigate failures. Those activities may be highly useful, but none automatically proves that a change meets the actual business need or is safe in production.

The most durable human checkpoints are usually at the beginning and end of the work:

Planning gate: Confirm the goal, scope, constraints, dependencies, and definition of done.
Verification gate: Confirm that the change meets acceptance criteria and that test evidence is meaningful.
Release gate: Decide whether the change is appropriate for the target environment and risk level.

Not every task needs the same approval process. A maintenance agent resolving a low-risk internal defect may be allowed to merge after deterministic checks pass. A change involving customer data, payments, security boundaries, or brand-critical behavior deserves more scrutiny. The decision is largely a matter of trust and consequences, not merely technical capability.

Supervise Coding Agents With Evidence, Not Guesswork

Supervising a coding agent takes more than reading its diff. The Levr Dashboard puts each issue, its acceptance criteria, linked tests, workflow gate status, and attributed agent activity in one view, so developers can check the evidence before they approve and see exactly who or what changed the work.

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

Every gate, test result, and agent action in one place, for the developer who signs off.

Why are quality gates more important than model choice?

Coding agent reliability depends on a system of constraints and verification, not only on choosing a stronger model or crafting a better prompt. Clear acceptance criteria, test requirements, controlled permissions, escalation paths, and human approval points create the conditions for trustworthy autonomous work.

Models can make mistakes, use stale assumptions, or pursue the wrong interpretation of a vague request. Better context and retrieval can reduce these failures, but no model choice removes the need to verify behavior. A practical definition of done should state more than “tests pass.” For high-risk changes, the gate should include evidence that is difficult to fake accidentally. GitHub’s protected branch rules provide a familiar example of enforcing review and status-check requirements before a branch can merge.

How can teams run coding agents without losing project visibility?

Teams retain visibility by making tasks, acceptance criteria, test evidence, pull requests, status changes, and agent actions part of the same project record. This prevents the plan from drifting away from work performed inside isolated coding tools, terminals, and automation pipelines.

One way to handle this in Levr’s agent-first control plane is to keep the issue, structured acceptance criteria, tests, workflow gates, and attributed activity connected. A coding agent can work against the same live project objects that humans inspect, rather than treating the prompt as its only source of truth.

A visible workflow should answer basic operational questions quickly: What was the original intent? Which agent or developer changed the work item? What tests were run, and against which branch? What failed, what was retried, and what remains blocked? Who approved the transition to done or release?

What does a practical path to greater autonomy look like?

A practical path starts with narrow, low-risk tasks and expands autonomy only after the team trusts the quality controls, observability, and recovery process. The correct autonomy level varies by system, but every stage should have measurable evidence, clear stop conditions, and a way for humans to take over.

Agent-assisted work: Developers use coding agents for explanation, drafts, tests, and local changes, while humans retain control of all decisions.
Agent-generated pull requests: Agents implement bounded tasks, run checks, and prepare a reviewable pull request with a summary of evidence.
Autonomous low-risk maintenance: Agents can resolve defined classes of internal defects when deterministic checks and rollback paths are in place.
Selective production autonomy: Agents act on specific production-adjacent tasks only after teams establish clear permissions, monitoring, and incident response ownership.

Technical Deep Dive FAQ

What is a coding agent?

A coding agent is software that uses a language model and development tools to perform engineering tasks such as reading repositories, proposing changes, editing files, running commands, writing tests, and preparing pull requests. Unlike autocomplete, a coding agent can often work through multiple steps toward a goal. Its usefulness depends on the context, tools, permissions, and constraints it receives.

What is agentic coding?

Agentic coding is a software delivery approach in which coding agents take on meaningful parts of planning, implementation, testing, investigation, or maintenance. It does not require fully autonomous software delivery. The defining characteristic is that agents can use context and tools to pursue multi-step outcomes, rather than only generating isolated snippets. Good agentic coding combines autonomy with explicit task definitions, quality gates, permission boundaries, and accountable ownership.

How do developers build judgment when coding agents do the implementation?

Developers build judgment by using coding agents as investigation tools rather than substitutes for understanding. By using agents to explain architecture, trace data flow, propose tests, identify risks, and compare implementation options, developers can validate claims and bring focused questions to the team. Engaging in design discussions, incident reviews, and release planning exposes developers to the earned knowledge that does not appear in generated code or documentation.

What should be in a coding agent definition of done?

A coding agent definition of done should specify the intended behavior, acceptance criteria, nonfunctional constraints, test expectations, and approval requirements. A stronger definition requires linked evidence, such as unit and integration results, regression coverage, CI/CD status, a pull request summary, and human confirmation for changes that cross a defined risk boundary.

How do quality gates improve coding agent reliability?

Quality gates improve reliability by converting vague trust into explicit conditions for progress. A gate can require an approved issue, structured acceptance criteria, passing tests, review completion, or a deployment check. This makes it harder for an agent to declare success based only on a plausible-looking code change, ensuring that both humans and agents are held to the same standard of evidence.

How can multiple coding agents share project context?

Multiple coding agents share project context most reliably through a common system of record for issues, acceptance criteria, related tests, decisions, workflow state, and activity history. Platforms using the Model Context Protocol, or MCP, can expose shared project objects to compatible agents, ensuring every agent works from the current task state.

Key Takeaways

Engineering is becoming less about typing routine code and more about building the judgment required to direct and validate increasingly capable systems.

The sustainable model combines coding agents, mentorship, visible workflows, and risk-based human oversight.

Focus on supervision: Strong engineers define intent, inspect evidence, manage exceptions, and make release decisions.
Preserve earned knowledge: Give developers access to real operational context, mentorship, and supervised ownership.
Build gates first: Tests, approval boundaries, escalation rules, and activity visibility matter more than autonomous demos.
Scale autonomy carefully: Start with low-risk work, measure outcomes, and expand only when quality and recovery processes are proven.

Developers should not compete with coding agents on keystrokes. They should learn to lead them responsibly.