The Agentic SDLC: Where Review and Remediation Fit

The agentic software development lifecycle assigns more execution work to coding agents while engineers retain decision authority. Agents break goals into tasks and generate code. Some agents also run checks against their own output. Engineers specify expected behavior and constraints, then decide whether a change qualifies for release.

Engineers therefore move from writing each implementation detail to directing work across the lifecycle. The engineer supplies repository context, defines acceptable behavior, and resolves uncertainty that an agent cannot settle safely. Code review and fixing become separate controls because generation and verification require different judgment.

Agent handoffs can lose intent even when the resulting code appears valid. The 2025 planner-coder and MAST findings attribute 75.3% of studied multi-agent generation failures to semantic breakdowns between planning and coding agents. The same research classifies 75.17% of observed failures as silent gray errors. Silent gray errors pass compilation or basic checks but violate the intended business logic.

The agentic SDLC gives engineering leaders a model for controlling those handoffs. Agents perform bounded work, while engineers define what success means and retain approval authority.

The lifecycle stage by stage: plan, write, review, test, ship, operate#

The agentic SDLC changes who performs work at each stage while keeping people accountable for the software. Port's lifecycle frame assigns agents work across planning, development, validation, release, and production operations. Engineers still set goals, define constraints, and decide whether a change can proceed.

During planning, agents can turn requirements into implementation tasks and identify affected components. Product and engineering leaders retain ownership of intent, architecture boundaries, and acceptance criteria. Clear planning context gives the coding agent a specification against which later reviewers can judge its work.

During writing, coding agents such as Cursor, Claude Code, and Copilot can inspect repository context, modify several files, and create tests with less step-by-step direction. Engineers still choose the technical approach and resolve design decisions that exceed the agent's authority. Repository rules and approved patterns limit how an agent implements the plan.

During code review, an independent agent can examine each change for defects and policy violations before a person reviews it. Backslash describes agent review as an immediate first pass, while people retain final approval, accountability, and mentorship. Human reviewers must judge whether the implementation matches the requested behavior and fits the codebase.

During testing, agents can create test cases, run existing suites, and investigate failures. Engineers define what acceptable behavior means and decide whether the evidence covers the intended change. A passing build confirms specific checks, but it cannot establish that the specification itself was complete.

During shipping, agents can prepare release artifacts and respond to deployment signals. Release owners retain the release policy, production judgment, and authority over acceptable blast radius. Automated pauses and rollback proposals can shorten the response path without transferring the shipping decision to an agent.

During operations, agents can correlate telemetry with recent changes and propose fixes. People retain incident command and final accountability for customer impact. Operators decide whether a proposed remediation addresses the cause and whether the fix should enter the normal review path.

Review and test strain first because coding agents can produce candidate changes faster than people and validation systems can assess intent. Review queues accumulate, while routine checks can miss behavior that conflicts with the specification. The agentic software development lifecycle therefore needs code review and fixing capacity that scales with generation.

Why validation becomes the bottleneck#

Coding agents increase generation capacity while validation remains bound by human attention, test runtime, and clear acceptance criteria. Each generated change creates review and test work. When generation outpaces validation, unverified changes accumulate in pull request queues.

Research published in 2025 attributed 75.3% of multi-agent code generation failures to the planner-coder gap. UC Berkeley's MAST analysis of more than 1,600 execution traces found that 75.17% of failures appeared as silent gray errors. An MIT study of 300 enterprise generative AI deployments found that 95% of pilot programs produced no measurable financial return. The planner-coder gap loses architectural intent when one agent hands a plan to another. Silent gray errors add a harder problem because the code can compile and pass superficial checks while violating business logic.

Manual debugging adds pressure at the same point in the lifecycle. The Stack Overflow developer survey covered in the same research found that developers often spend more time debugging AI-generated code than writing the code themselves. Reviewers therefore inherit more than additional pull requests. They inherit changes that look plausible, require context to assess, and can demand extended investigation before approval.

Independent validation must scale with generation capacity. Compilation and basic tests filter obvious failures, but they cannot confirm that a change satisfies its intended behavior or fits the codebase. Review and testing become the constraint because each accepted change still requires evidence that the implementation matches the plan.

What an independent review layer must do#

An independent review layer must apply the same code review policy regardless of authorship. Code from Cursor, Claude Code, Copilot, or a human developer should face the same acceptance criteria. Separate review also reduces the chance that a coding agent will repeat assumptions or reasoning errors while assessing its own output.

A successful compile confirms that code satisfies language and build requirements. It cannot confirm that the implementation matches the requested behavior. Business-logic verification, as tested by MAST, compares the implementation with the specification, constraints, and expected outcomes. That comparison can catch code that runs correctly while calculating the wrong value, applying a rule in the wrong order, or mishandling an edge case.

A rigorous review layer should assess six domains. Security review finds exploitable behavior and unsafe data handling. Correctness review checks intent, state changes, and failure paths. Maintainability and performance review examine whether the change creates avoidable complexity or resource costs. Architecture and operations review check service boundaries, deployment behavior, observability, and recovery requirements.

Useful findings also need enough evidence to support fixing. Each finding should identify the affected code, explain the failure mechanism, connect it to an engineering rule or intended behavior, and provide a reproducible basis for verification. Engineers can then distinguish actionable defects from low-confidence suggestions before remediation begins.

How autonomous remediation fits without breaking the merge gate#

Autonomous remediation closes the delay between code review and fixing by turning each finding into a verified pull request. The workflow lets automated review find and fix issues at agent speed while engineers retain authority over what ships.

The remediation service first identifies a finding and records the affected code and expected behavior. The service then generates a bounded fix. Verification reruns relevant tests and analysis to confirm that the change resolves the finding without introducing a detected regression. After verification passes, the service creates a pull request with the change and supporting evidence. An engineer reviews the pull request and decides whether to merge it. Fixes never auto-merge.

The pull request keeps the existing merge gate intact because the repository still applies its normal approvals, checks, and branch policies. A failed verification produces no fix pull request for review. A rejected pull request also leaves the codebase unchanged, while the rejection can inform a revised fix. Engineers keep final accountability without manually translating every valid finding into code.

Where Hyrax fits in the agentic SDLC#

Hyrax provides an independent code review and fixing layer for the agentic software development lifecycle. It reviews all code, including AI-written code, without depending on the coding agent that produced it. Hyrax checks security and correctness. Its review also covers maintainability, performance, architecture, and operations.

Hyrax organizes this work through Scan, Fix, Improve, and Govern. Scan finds issues across the codebase, while Fix creates verified fix pull requests for engineer review. Improve addresses code quality over time. Govern applies review policies across repositories.

Hyrax complements Snyk, Sonar, and CodeRabbit rather than replacing them. Each product can keep its existing role in the development pipeline. Hyrax does not ingest findings from other scanners. It performs its own analysis and submits its own fixes.

The credit-based Free and Paid plans provide practical entry points for adding Hyrax to an agentic SDLC. Engineering leaders can choose a plan based on the required usage and workflows.

FAQs#

How does the agentic SDLC differ from a traditional SDLC?

A traditional SDLC assigns most engineering decisions and implementation work to people. An agentic SDLC lets coding agents execute multi-step tasks while engineers define intent, constraints, acceptance criteria, and approval gates.

Can coding agents review their own code?

Coding agents can inspect their own output, but shared context can cause a reviewer agent to repeat the writer agent's assumptions. Independent code review provides a separate judgment, while an engineer retains final approval.

What happens when an engineer rejects a verified fix PR?

A rejected fix PR leaves the codebase unchanged and preserves the merge gate. The finding can remain available for manual fixing or a revised fix based on reviewer feedback.

How does AI code review relate to full-lifecycle review?

AI code review examines code changes for defects, risks, and policy violations. Full-lifecycle review applies verification across the agentic software development lifecycle, including whether implementation matches planning intent and whether production evidence calls for further fixes.

The takeaway for engineering leaders#

Faster code generation produces unverified risk unless independent code review keeps pace. Engineering leaders should own review standards, merge policy, and the evidence required to accept fixes. Tools can find issues and prepare remediation, but engineers should retain approval authority.

The agentic SDLC succeeds when generation speed remains subject to independent verification and human accountability.

Ship clean code.