What is agentic development?

AI coding agents have moved from weekend experiments to production workflows. A developer can now assign a multi-step task to a tool like Claude Code, Cursor, Codex, or GitHub Copilot and receive working code minutes later, without touching most of the implementation. That shift changes what a developer's day looks like, and it changes what engineering leaders have to govern.

This page explains what agentic development is, how agents participate across coding, testing, and remediation, and how the workflow differs from earlier forms of AI assistance. It also covers the risks that come with autonomy—review, ownership, and reliability—and the practices teams use to adopt it responsibly. If you lead an engineering organization or write code inside one, agentic development is already reshaping how work moves through your software development lifecycle.

What is agentic development and how does it work?

Agentic development is an approach to building software in which AI agents autonomously carry out multi-step coding tasks—planning, writing, testing, and fixing code—while a software developer defines the goal and reviews the result. The agent operates with a degree of independence: it decides the sequence of actions, calls tools, reads files, and iterates toward a working solution rather than returning a single suggestion.

In practice, a software developer states an objective in natural language, and the agent breaks that objective into steps it executes on its own inside a sandboxed environment. It may create files, run tests, read the surrounding codebase, and correct its own errors before presenting a pull request. The human role shifts from writing each line to setting direction, supplying context, and judging whether the output meets the bar.

The defining trait is autonomy over a task, not a keystroke. Where a code-completion tool waits for you to accept a suggestion, an agent takes the wheel for a stretch of work and reports back when it believes the job is done.

Why is agentic development growing so fast?

Agents are already responsible for a large and growing share of production code. AI agents assist in generating 40% or more of committed enterprise code, and adoption is climbing: 55% of developers now regularly use AI coding agents (Pragmatic Engineer, “AI Tooling for Software Engineers in 2026,” 900 respondents). . This is no longer a fringe practice at the edge of the organization.

The volume changes the shape of the work. Pull requests that used to run around 300 lines now routinely reach 3,000 or more, and manual review cannot scale to match. When a single change is ten times larger and arrives ten times faster, the human safety net that engineering teams relied on for years starts to tear.

The quality stakes are concrete, not hypothetical. Independent research from Carnegie Mellon studied 807 Cursor-adopting repositories and found a temporary velocity spike that faded by the third month, replaced by a persistent 30% rise in static analysis warnings and a 41% increase in code complexity. Speed borrowed early is repaid later in technical debt that slows the same teams down.

There is a human factor that compounds the technical one. Research cited in the paper—"Measuring and Mitigating Overreliance is Necessary for Building Human-Compatible AI"—finds that the more developers work with AI, the more likely they are to trust its output, even when they have the expertise to catch its mistakes. Autonomy raises the volume of code exactly as the instinct to scrutinize it declines.

How do AI agents participate in coding, testing, and remediation?

Agentic development spreads AI across three activities that were once distinctly human. Each is a different mode of participation, and each carries its own failure surface.

Coding

The agent writes new code and modifies existing code to satisfy a stated goal. It reads relevant files, infers patterns from the surrounding source code, and generates implementations across multiple files in a single task. Because it works without seeing the full architecture, it can produce output that runs correctly in isolation while violating structural boundaries or conventions elsewhere in the system.

Testing

Many agents generate and run tests as part of completing a task, using the results to check their own work and iterate. This self-checking is useful for catching obvious breakage, but it is not independent verification—the same model that wrote the code is judging whether the code is correct, so both share the same blind spots. Passing tests written by the agent tell you the code does what the agent expected, not what your standards require.

Remediation

When verification surfaces an issue, a SonarQube Remediation Agent can generate a fix, validate it, and open a pull request for review. This closes the loop between finding a problem and resolving it, and at agent speed it can work through backlogs of accumulated issues that teams never find time to clear manually. The value depends on the accuracy of what feeds it: a SonarQube Remediation Agent working from noisy findings spends its effort chasing false alarms.

How is agentic development different from standard AI coding assistance?

Agentic development is not simply a more powerful version of autocomplete. The distinction matters because the two demand different controls, and treating an agent like a smarter assistant is how teams lose visibility over what ships.

Standard AI assistance

Earlier AI-assisted development tools operate at the level of a suggestion. In an IDE, they complete a line, propose a function, or answer a question, and the developer accepts, edits, or rejects each one in real time. The human stays in the loop for every change, and the unit of output is small enough to read at a glance. Control is continuous, and accountability rarely leaves the developer's hands.

Agentic development

An agent operates at the level of a task. It runs asynchronously, often for minutes or longer, making a chain of decisions and edits before surfacing a result. The developer sets the goal and reviews the outcome but does not approve each intermediate step. The unit of output is a batch—frequently a large one—so the reviewer confronts a finished body of work rather than a stream of small choices.

Key distinction

The dividing line is where human judgment sits. Standard assistance keeps a person in the loop at every step; agentic development moves the person to the boundaries, setting direction at the start and judging results at the end. That shift is what delivers the velocity, and it is also what makes independent code verification essential—because the intermediate steps no one watched still reached your main branch.

What are the risks of using AI agents to write production code?

Autonomy is the source of both the value and the exposure. The risks below are not arguments against agentic development; they are the specific gaps a responsible adoption has to close.

Review at scale

Human review was designed for small, frequent, iterative commits. Agent pull requests are routinely ten times larger and arrive faster than any reviewer can meaningfully absorb. Reading a 3,000-line change with the same rigor once applied to 300 lines is not realistic, so unverified issues slip through and accumulate. Review does not disappear as a requirement—it disappears as something a human can perform alone.

Ownership and accountability

An agent generates the code, but a person is still accountable for what it does in production. That gap between authorship and accountability is new. Developers who did not write a line are responsible for its security, its behavior, and its maintenance, which makes a consistent standard applied to all code—regardless of who or what produced it—the only defensible basis for ownership.

Reliability

Reliability does not improve automatically as models get more capable. Princeton researchers who tested 15 models across two reliability benchmarks, reported in "Towards a Science of AI Agent Reliability"—found that recent capability gains yielded only small improvements in reliability. Hallucination is structural to how large language models work: they are optimized to produce plausible output, not to signal uncertainty. More capable agents write larger pull requests, which raises the stakes of unverified output rather than lowering them.

How can teams adopt agentic development safely?

Adopting agents safely is less about restraint than about building the right controls into the workflow so speed does not come at the cost of trust. The practices below share a common logic: guide agents before they write, verify everything they produce, and fix what verification finds.

Give agents context before they write

Agents make probabilistic assumptions about anything they do not know, and without authoritative context they produce code that is functionally correct but contextually wrong. Supplying architectural constraints, coding standards, and compliance requirements up front narrows that gap. Too little context yields code that ignores your standards; the right context, selected per task, keeps output aligned from the start.

Verify every change independently

Verification in agentic development must be zero trust and multilayered: it uses a different method than the one that generated the code, keeps a clear segregation of duties, and stays auditable and repeatable. No single check catches everything, so deterministic analysis, security scanning, and architectural review work together. Independence is the point—an AI model checking another model's output shares the same failure modes and cannot serve as a trustworthy gate.

Automate remediation and keep humans accountable

When a quality gate fails, automated remediation can generate and validate a fix without waiting on scarce review bandwidth, while a developer retains final approval before anything merges. Feeding verification results back into the guidance step makes each cycle produce cleaner output than the last. The discipline is continuous, not a single gate bolted onto the end of the pipeline.

Standardize across tools

The average organization runs several AI coding tools at once, and each carries its own implicit quality bar. Applying one consistent verification standard across every agent—independent of which one wrote the code—is what keeps quality predictable as the tooling changes underneath you. This also produces the auditable trail that governance and compliance require.

How does SonarQube help you adopt agentic development safely?

SonarQube is the independent verification layer for agentic development. It gives every change the same consistent, explainable verdict, whether a developer or an agent wrote it, before the code reaches your main branch. That consistency comes from deterministic analysis: the same code produces the same findings every time.  Because verification runs separately from whatever generated the code, using a different method with a clear segregation of duties, the result is auditable and consistent no matter which agent produced the work.

At its core, SonarQube runs deterministic analysis across 40+ languages and frameworks, examining syntax, data flows, dependencies, and architecture rather than pattern-matching alone. You define what "acceptable" means through quality profiles, and enforced quality gates deliver a pass or fail decision on every change. In Sonar's own measurement, SonarQube operated at a 3.2% false-positive rate in 2025, which matters operationally: a low false-positive rate means developers act on real issues instead of learning to ignore warnings, and any automated remediation works on genuine problems rather than red herrings.

For agentic workflows specifically, SonarQube meets agents where they work. The SonarQube MCP Server exposes SonarQube's analysis as native tool calls for MCP-compatible agents such as Claude Code and Cursor, so verification runs inside the agent's loop rather than only after a pull request lands. SonarQube Advanced Security extends coverage to the software supply chain with dependency-aware SAST and software composition analysis, catching the outdated or vulnerable dependencies agents suggest based  on their training data. These capabilities map to Sonar's Guide, Verify, and Solve approach to agentic development, with SonarQube serving as the verification foundation the rest builds on.

To get started, teams typically connect SonarQube to their existing repositories and CI/CD workflow, define a quality gate, and route agent output through the same analysis applied to every other change.

Next steps

• The AC/DC—The framework for guiding, verifying, and solving across agentic workflows, and where agentic development fits within it.
• AI-assisted software development—How AI assistance evolved into autonomous agents, and what changed along the way.
• SonarQube product overview—Product documentation on deterministic analysis, quality gates, and language coverage.
• Reducing technical debt—Why agent-generated code accumulates debt and how to keep it in check.
• Automated code review—How static analysis catches bugs and vulnerabilities in code from any source.
• DevOps and CI/CD—Where verification fits in the pipeline agents now feed at scale.