AI for engineering
AI-SDLC implementation
Delegate more work to AI agents without losing control over important decisions.
We integrate AI agents into the development process, from requirements through testing and reviews, using your existing tools while keeping accountability with the team.
What is AI-SDLC?
AI-SDLC is a way to integrate artificial intelligence agents into the software development lifecycle.
Agents can help prepare tasks, implement changes, generate tests, and support technical reviews. To use them responsibly, teams need to define the context, working rules, and decisions that remain under human control.
At Coorva, we help organizations incorporate these practices into the engineering processes they already use.
Context before execution
Agents need clear objectives, specifications, acceptance criteria, and project knowledge.
Validation throughout development
AI-generated code must go through the tests, checks, and reviews appropriate to the change.
Human accountability
Important engineering decisions must retain clear ownership and explicit approval criteria.
The Risk-First approach
Not all software changes have the same consequences. A small, reversible modification does not require the same controls as a change involving security, architecture, or sensitive data.
Risk-First helps assess the exposure of each change before deciding how much work to delegate to AI and which validations to require.
Risk-First defines the level of control. AI-SDLC incorporates it into the development process.
- 01
Readiness
Ensure the task has enough information to begin.
- 02
Exposure
Consider the impact, uncertainty, and reversibility of the change.
- 03
Autonomy
Define what the agent can do and when human intervention is required.
- 04
Evidence
Determine which tests, checks, and reviews are needed to assess the result.
- 05
Accountability
Make clear who accepts the relevant decisions and risks.
How the cycle works
Integrating AI agents requires preparing the work, defining execution conditions, and evaluating results before accepting them.
- 01
Define the context
Establish the project's objectives, scope, constraints, and technical standards.
- 02
Prepare the tasks
Organize requirements and acceptance criteria so the work can be understood and executed.
- 03
Assess exposure
Analyze the possible consequences of a change, its uncertainty, and its reversibility.
- 04
Define autonomy
Agree on the agent's permissions, required validations, and decisions that need approval.
- 05
Implement and verify
Agents can develop code, generate tests, and prepare changes for review, following the agreed-upon rules.
- 06
Approve and learn
The team reviews the evidence, makes the appropriate decisions, and evaluates the outcome to improve the process.
Three ways to delegate work to AI
Autonomy depends on the characteristics of the change and the controls available. The same level of oversight should not be applied to every task.
Autonomous execution
For limited, reversible, and well-specified changes, agents can execute tasks within previously defined permissions and verification requirements.
Examples: documentation, mechanical refactors, or local changes with reliable tests.
Supervised execution
The agent can implement changes and prepare tests, but a person reviews technical or business aspects before accepting the change.
Example: a functional change that requires validating permissions, business rules, or integrations.
Human decisions
When a change has significant consequences, the team retains authority over decisions and approvals. AI can assist with analysis or implementation within agreed-upon boundaries.
Examples: critical architecture, security, sensitive data, or changes that are difficult to reverse.
These categories are illustrative. The level of control depends on the context and exposure of each change.
Illustrative example
A concrete example
Add an endpoint to cancel an order
An AI agent can help implement the change, generate tests, and prepare a pull request.
Before accepting the change, the team must review business-specific aspects such as permissions, order statuses, concurrent operations, and possible effects on other processes.
The agent's autonomy and the necessary validations are defined according to the change's exposure.
AI agent
- Implementation
- Tests
- Pull request
Engineering review
- Permissions
- Business rules
- Effects of the change
We work with the tools your team already uses
Implementing AI-SDLC does not require replacing your backlog, repositories, or continuous integration and delivery tools.
Depending on the project context, we can incorporate agents such as Claude Code or Codex, specification tools such as Spec Kit, and existing engineering validations.
The goal is to integrate these capabilities into a coherent process, not to add unnecessary tools.
Backlog and requirements
Tasks and priorities remain in the team's management tools, such as Jira.
Specifications and context
Requirements, plans, and technical criteria are organized using practices and tools suited to the project.
Development agents
Existing agents participate in implementation, testing, and preparing changes.
Repositories and validations
GitHub and CI/CD remain part of the review, verification, and delivery process.
How we support implementation
We start with an existing engineering process and work with the team to introduce AI agents progressively.
- 01
Choose a specific process
We identify a development workflow where AI agent participation is relevant and define the initial scope.
- 02
Agree on rules and responsibilities
We establish the context, acceptance criteria, autonomy levels, and required validations.
- 03
Implement and evaluate
We incorporate these practices into the team's work, observe how they perform, and assess where expanding them makes sense.
AI adapts to the engineering process. Not the other way around.
Have a question about AI-SDLC?
If you'd like to learn more about this approach or how it could apply to your team's development process, get in touch.
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