AI-Assisted Delivery Playbook
The AI-Assisted Delivery Playbook
AI-assisted development works best when a team runs it as one repeatable operating model rather than as a set of separate practices. This playbook assembles the handbook into a fourteen-step workflow that takes a feature from business problem to production, then shows how sprint ceremonies change, how to start new projects and how to share what teams learn. At Azumo, that operating model is the Azumo SDLC.
Throughout this handbook, we have examined AI-assisted development through individual lenses: how to write effective prompts, how to test AI-generated code, how to structure reviews, how to manage security, how to prepare repositories and how to set policy. Each chapter addresses one piece. But pieces are not a system. The teams that achieve the most with AI coding assistants are the ones that have assembled these practices into a coherent, repeatable operating model.
This chapter turns the handbook into a delivery playbook: a step-by-step workflow that a team can adopt, adapt and refine. It is not prescriptive in the sense that every step must be followed mechanically. It is prescriptive in the sense that every step exists for a reason, and skipping steps without understanding why they exist tends to produce the problems those steps were designed to prevent.
The Fourteen-Step Delivery Workflow
The core of the playbook is a fourteen-step workflow that takes a feature from business problem to production deployment. The steps are not equal in duration or complexity. Some take minutes. Others take hours. But each one matters, and the sequence matters.
Step one is to start with the business problem, not the technical solution. Before any code is written and before any AI prompt is drafted, the team must understand what outcome the work is meant to produce. What user problem does this solve? What business metric does it affect? What does success look like from the customer's perspective? This sounds obvious, but AI tools make it dangerously easy to skip straight to implementation. A developer can describe a feature to a coding agent and have working code in minutes. Whether that code solves the right problem is a question only the business context can answer.
Step two is to define acceptance criteria and user acceptance testing scenarios. These are the concrete, verifiable conditions that determine whether the work is done. Acceptance criteria are the contract between the business and the engineering team, and in AI-assisted development they do a second job: they become the specification that guides AI code generation and the test cases that verify AI output. Well-written acceptance criteria are the highest-leverage input to an AI-assisted workflow because they constrain the solution space and define success clearly.
Step three is to identify architecture constraints. Before the AI generates code, the developer must understand where the new work fits in the existing system. Which services are involved? Which databases? What are the API contracts? Are there performance requirements, security boundaries or compliance constraints? This context keeps the AI from producing code that is sound in general but wrong for your system. The repository's AGENTS.md and architecture documentation, described in building the AI-ready repository, should provide much of this context, but the developer's judgment about the specific constraints of the current task cannot be replaced.
Step four is to break the work into AI-sized tasks. Large, ambiguous tasks produce poor AI output. Small, well-defined tasks produce good AI output. Much of the skill in AI-assisted development is decomposition: turning a feature that would take a developer two days into a sequence of tasks an AI can complete in focused, verifiable increments. Each task should have a clear input, a clear expected output and a way to verify correctness. A task like "build the user dashboard" is too large. A sequence like "create the dashboard data service that aggregates metrics from the analytics API, then build the dashboard API endpoint that returns aggregated data, then implement the dashboard component that displays the data with loading and error states" gives the AI clear scope for each step.
Step five is to create or update context files for the current work. If the task involves a part of the codebase with conventions not captured in the repository-level context, add a subdirectory AGENTS.md or update existing context files. If the task introduces a new pattern, document it before implementation so the AI follows the intended pattern rather than inventing one.
Step six is to use AI to inspect before changing. Before writing new code, ask the AI to read and explain the relevant existing code. This does two things. It loads the necessary context into the AI's working memory, which improves the quality of what it generates next. And it tests the developer's understanding of the current state, catching misconceptions before they become bugs. This inspect-first pattern is one of the most reliable practices in AI-assisted development.
Step seven is to generate or update tests. Following the approach in test-first development with AI, use the AI to write tests based on the acceptance criteria before implementing the feature. These tests are executable specifications: they define what the code should do in precise, machine-verifiable terms. The AI is particularly effective at generating test cases because it can enumerate edge cases and boundary conditions more systematically than most developers do by hand.
Step eight is to implement in small increments. Rather than generating an entire feature in one pass, implement it in small, testable pieces. Each increment should be small enough that the developer can review it thoroughly, the tests can verify it completely and any problem can be isolated quickly. Small increments also let the developer correct the AI's approach before errors compound across a large change.
Step nine is to run local and CI tests after each increment. Testing is not a phase that happens after implementation. It is a continuous verification loop that runs throughout. Every increment should pass existing tests (confirming nothing is broken) and new tests (confirming the increment works). Coding agents such as Claude Code can run tests directly as part of their workflow, creating a tight feedback loop where failures are caught and corrected immediately.
Step ten is an AI first-pass review. Before a person reviews the code, use an AI tool to perform an initial review pass. AI reviewers are effective at catching common issues: inconsistent error handling, missing input validation, unused imports, style violations and obvious logic errors. This first pass raises the quality floor before human review, so human reviewers can focus on architectural fit, business logic correctness and edge cases that require domain knowledge.
Step eleven is human review. Human code review remains essential in AI-assisted development. The reviewer brings context that AI tools do not have: understanding of the business domain, knowledge of ongoing initiatives that might conflict, awareness of organizational priorities and judgment about whether the technical approach fits the team's long-term direction. Human review is where the work is evaluated for judgment as well as correctness. The reviewer asks whether this is the right solution, not just whether it works.
Step twelve is QA and user acceptance testing. Formal quality assurance and user acceptance testing verify that the feature works as intended from the user's perspective. This step catches integration issues, usability problems and gaps between what the acceptance criteria specified and what users need. AI-generated code passes unit tests reliably. Whether it delivers the right user experience is a question that requires human testing.
Step thirteen is safe deployment. Features enter production behind feature flags, through canary deployments or with automated rollback. This is standard practice for modern software delivery, and it matters more in AI-assisted development because the speed of AI-generated code means more features are ready to deploy in any given sprint. Safe deployment practices keep the increased velocity from turning into increased risk.
Step fourteen is monitoring and learning. After deployment, the team monitors the feature for errors, performance issues and user feedback. The team also reflects on the AI-assisted workflow itself. Which tasks did the AI handle well? Where did it struggle? What context was missing? What would improve the process next time? These observations feed back into context files, prompt templates and team practices, creating a continuous improvement loop.
How the Azumo SDLC Puts the Workflow into Practice
The Azumo SDLC is Azumo's way of working with coding agents, written down once and shared by every engineer. It is a shared kit of workflows, skills, rules and review checklists, and the same setup loads in Claude Code, Qwen Code, OpenCode and Pi. It puts the workflow above into practice like this:
- Five workflows. Feature, bug fix, small change, docs and spike (experiment). The feature workflow runs clarify → spec → plan → build → validate → review → deliver.
- People decide at three points. The agent stops where a person should decide: approval after the spec, after the plan and before shipping.
- Evidence before anything passes review. Separate review checklists, for example for correctness, security, test adequacy and UI/UX, check the work. Work missing a test goes back to build.
- One commit per verified task on a feature branch.
- Context lives with the project, not in one chat. Handoffs and decisions are saved to shared project memory. Agents propose; people approve.
- Teams extend the kit without changing it. A team can add its own steps and checks, such as an accessibility check, without changing the shared kit.
- Every role contributes. Business analysts, project managers, designers and DevOps engineers write down what they know as skills (how to do a step) and rubrics (what good looks like).
- Work runs in parallel. Bugs, features and experiments run in parallel.
How Sprint Ceremonies Change
The fourteen-step workflow does not replace agile ceremonies. It changes them. The sprint does not disappear. It becomes the coordination layer that sequences AI-assisted work and keeps engineering aligned with the business.
Sprint planning shifts from estimating effort to reviewing specifications. When AI handles more of the implementation, the bottleneck moves from coding to clarity. Planning sessions focus on whether acceptance criteria are precise enough to drive AI-assisted development, whether architecture constraints are well understood and whether the work has been decomposed into appropriately sized tasks. The question changes from "how long will this take to build?" to "is this specified clearly enough for AI-assisted delivery?" Effort estimates still matter for budgets; for how they work on an Azumo engagement, see how Azumo prices custom software.
Daily standups become shorter and more focused on blockers and coordination. When developers work with AI assistants, progress on individual tasks speeds up. The standup focuses on integration points, dependencies and decisions that affect several team members rather than status updates on individual implementation progress.
Retrospectives gain a new dimension: improving the AI workflow. The team reviews not just what went well and what did not in delivery outcomes, but also how the AI-assisted workflow performed. Which context files need updating? Which tasks suited AI assistance and which did not? What new patterns emerged that should be captured as prompt files or skills? Retrospectives become the main mechanism for improving the team's AI-assisted delivery over time.
Sprint demos become reviews of verification evidence. Instead of demonstrating a working feature, which the product owner has likely already seen through continuous deployment, the demo presents the evidence: test results, review findings, performance benchmarks and user acceptance testing outcomes. In AI-assisted development, building the feature is the easy part. Proving that it works correctly is the valuable part.
The Accelerator Model for New Projects
New projects are a good fit for AI-assisted delivery because there is no legacy context to manage. The accelerator model front-loads the activities that make AI-assisted development productive: creating context files, establishing testing infrastructure, documenting architecture decisions and building prompt templates for common development tasks.
A typical accelerator engagement begins with a day of architecture and specification work. The team defines the system architecture, establishes coding conventions, sets up the repository with AGENTS.md files and other context files, creates the CI/CD pipeline with appropriate quality gates and writes prompt files for the project's recurring development patterns. This preparation lets the rest of the project run at AI-assisted speed because the AI tools have the context they need from the start. When you hire a custom software development company for a new build, ask what it sets up before the first feature is written.
The accelerator model also sets the team's operating rhythm: how tasks are decomposed, how context files are maintained, how reviews are conducted and how the team captures what it learns. By the end of the first sprint, the team has working software and a delivery system it can use for the rest of the project. On Azumo projects, a well-scoped app typically reaches its first production release in 2–4 weeks, depending on requirements; larger systems ship as a series of 2–4 week releases.
Cross-Project Learning
One of the most valuable and most overlooked parts of AI-assisted delivery is cross-project learning. When a team discovers an effective workflow pattern, prompt template or context file structure, that knowledge should spread across the organization, not stay locked in a single project.
Cross-project learning takes several forms. Template repositories capture proven project structures, context files and CI/CD configurations that new projects can fork and customize. Prompt libraries collect tested prompt files for common development tasks, organized by technology stack and problem type. Workflow playbooks document how specific types of projects run their AI-assisted delivery, including sprint ceremony formats, review checklists and deployment strategies.
The organizational learning loop connects individual project retrospectives to shared knowledge bases. When a team on one project finds that a particular approach to testing AI-generated database migrations works well, that approach becomes available to every other team. When another team finds that a specific AGENTS.md structure prevents a class of common errors, that structure becomes a template for future projects. In the Azumo SDLC, the shared kit plays this role: it is written down once and shared by every engineer, and project teams add their own steps and checks on top of it.
Cross-project learning is what separates organizations that use AI tools from organizations that have an AI-assisted delivery capability. The former have individual developers who are productive with AI. The latter have an organizational system that makes every developer more productive and improves over time.
Putting It Together
The AI-assisted delivery playbook is not a new methodology. It is a disciplined application of proven software engineering practices, adapted for a world where AI handles more of the implementation and people handle judgment. The fourteen-step workflow makes sure every feature is specified before it is built, tested before it is merged, reviewed before it is deployed and monitored after it is released. Sprint ceremonies coordinate the work and capture what the team learns. The accelerator model gets new projects productive quickly. Cross-project learning spreads individual team improvements across the organization.
The teams that succeed with AI-assisted development are not the ones with the best tools or the most advanced prompting techniques. They are the ones with the clearest specifications, the most reliable test suites, the most careful review processes and the strongest feedback loops. The playbook makes these practices explicit, repeatable and improvable.