Agentic Engineering: AI Shifts Dev from Code to Review

An AI agent successfully completed a software development task without any human coding intervention. The agent autonomously managed the entire process, including planning, writing the code, running tests, and finally opening a pull request.

This automation streamlined the workflow so that human involvement was limited to a quick review and a merge action. The task, which typically takes a developer one to two days, was completed in just 11 minutes.

Cognition's Deployed Engineer, Sumit Shatwara, highlighted the speed and efficiency, stating, "An AI agent picked up the ticket or the prompt... and agent did all the stuff... the planning writing the code running the test and finally opening a PR and just developer just uh read it and ask one question and then click merge and that review took around 11 minutes."

Levels 0 to 2 of AI integration primarily focus on enhancing individual productivity, whereas levels 3 to 5 aim to scale the impact across an entire team.

This significant progression is not achieved merely through larger AI models but by adopting an entirely different operational framework. Moving into the L3+ domain requires AI to manage the entire codebase and handle complex, delegated tasks, thereby enabling a new level of team collaboration and efficiency.

Specifically, the review and verification processes become immensely valuable, serving as crucial quality gates.

This means that while AI handles more tasks, the responsibility and impact of human reviewers grow proportionally. Sumit Shatwara underscored this, noting, "Every rung up this letter removes a human checkpoint which really raises the value of the checkpoints that remain and that is the verification that is the review and that is you all which you are in the room."

Understanding AI agents involves distinguishing between local and cloud-based models. Local agents operate synchronously within a developer's environment, integrated with their editor and file system, and are directly controlled by the user. They function primarily as developer tools.

In contrast, cloud agents act as autonomous team members, with their own machines capable of performing the full development lifecycle asynchronously. This architecture enables parallel delegation of tasks, leading to substantial productivity gains for development teams.

Micromanaging AI agents, likened to a nervous parent watching a child learn to drive, nullifies the productivity advantages that AI offers. Successful delegation, therefore, depends on trusting the agent with clearly defined tasks and avoiding unnecessary interference with intermediate steps.

This principle serves as the ultimate filter for every piece of work considered for a cloud agent.

Tasks that can be objectively verified allow AI agents to iterate and refine their solutions indefinitely, ensuring consistent quality. Conversely, tasks requiring subjective human judgment may necessitate manual refinement or a hybrid human-AI workflow, where human input guides the process at critical junctures.

The delegation boundary for AI agents is precisely equivalent to the coverage boundary of existing test suites.

This perspective reframes the bottleneck from AI capabilities to the robustness of testing infrastructure. Consequently, quality assurance (QA) engineers and those responsible for building verification systems become pivotal in determining the speed and safety of AI integration, setting the 'speed limit' for agentic engineering adoption.

Within this sandbox, Devon autonomously plans and implements code.

The agent handles end-to-end tasks, encompassing planning, code implementation, building, and testing, including visual regression and unit tests. After successfully completing these steps, it creates a pull request (PR) ready for human review, significantly streamlining the development process.

This involves removing the human from the initiation phase of tasks, shifting their role from a trigger to a final gatekeeper.

Key application patterns include automated vulnerability remediation, bug investigation triggered by continuous integration (CI) failures, and proactive monitoring via platforms like Slack. Agents automate the entire 'triage to PR' phase, saving developers 15-20 minutes of manual effort per incident. This fundamental shift frees developers to focus on higher-level decision-making and final approvals.

The primary goal of agentic engineering is not to replace human developers but to empower them by automating mundane tasks.

This allows engineers to concentrate on critical review and decision-making, leveraging their expertise where it matters most. The integration of human gates ensures that automated processes maintain high quality and align with strategic objectives.

This comprehensive approach allows the agent to understand and investigate complex issues.

The AI agent maps function dependencies, analyzes database logs, and conducts thorough root cause analysis. This capability enables Devon to automatically identify HTTP 500-level errors and track their frequency without human intervention, significantly accelerating the debugging process.

This speed is a significant improvement compared to manual human resolution, which typically takes 30 minutes to several hours.

The system handles every step, including writing tests and ensuring continuous integration (CI) passes, before proposing a fix. This rapid resolution minimizes downtime and operational impact.

The platform automates task execution, allowing humans to retain their role as the final gatekeepers for critical decisions.

This approach ensures that while AI handles the heavy lifting, human expertise is preserved and optimized for high-value judgment. The system is designed to run hundreds of parallel sessions, thereby amplifying overall productivity without diminishing the essential human role.

This comprehensive integration spans deep documentation, requirements planning, execution, and review.

The platform leverages isolated virtual machines (VMs) that support both Linux and Windows environments, with upcoming support for Mac OS. The overarching principle for all these agentic surfaces remains human approval at the final gate, ensuring quality and control.

This capability significantly enhances the efficiency and reliability of the development pipeline.

The agent provides video evidence and detailed test reports upon successful completion, ensuring transparency and traceability. This proactive management of CI/CD logs and test failures minimizes manual intervention and accelerates deployment cycles.

Devon can complete the entire Software Development Life Cycle (SDLC) process within 10 to 30 minutes, depending on the job's complexity. Tasks that typically take a human developer 3 to 6 hours can be accomplished by Devin in just 30 minutes.

The total time for AI-driven development is primarily influenced by the complexity of success criteria and the scope of the task. For instance, security-related full codebase scans may still require several days, illustrating the scalability of AI's application.