5 Steps to Successfully Integrate AI Agents into Product Development

AI agents have already become an integral part of development in many IT companies, promising faster processes, fewer errors, and freeing developers from routine tasks. But are they really as effective as their creators claim?

At Waites, we develop and maintain a product that uses IIoT, ML, AI, and cloud technologies to detect deviations in industrial equipment performance and prevent failures. My team has gained hands-on experience integrating GitHub Copilot Agent and other tools into daily workflows.

In this column, I want to share our experience and outline steps that can help implement AI agents into routine processes so they become genuine assistants rather than sources of problems.

Do AI agents really speed up development?

AI agents are often promoted as near-autonomous developers: they can write code, generate tests, perform code reviews, optimize performance, and even create full application prototypes. For example, GitHub Copilot Agent can analyze a project’s structure, adapt to a developer’s style, and propose ready-made solutions — from unit tests to refactoring.

From my team’s experience, Replit Agent excels at creating demo projects that can be used to validate business ideas. GitHub Copilot Agent performs well in frontend projects using Node.js, TypeScript, and JavaScript: the agent handles code review, writes tests, and comments on Pull Requests, allowing team leads to quickly review and approve changes. Productivity noticeably improves: testing and reviews are faster, and developers spend less time on routine tasks.

At the same time, backend projects in PHP or Python show less consistent results: the agent struggles with legacy code, large files, or non-standard architectures, sometimes generating errors that break tests.

I agree that AI agents have huge potential, but I don’t believe they can replace developers yet. They are assistants that speed up work, but they require constant human oversight — especially considering security standards like ISO/IEC 27001 or SOC2. If you want agents to meaningfully boost team productivity, the key is proper configuration and training your team to use them effectively.

Practical steps for integration

Without proper integration, training, and oversight, AI agents quickly become mindless tasks. Our experience at Waites confirms this. When we first connected GitHub Copilot Agent to our work environment, the first few weeks were challenging. While the agent was adapting to each developer’s style and the project, it produced numerous errors. Later, after we understood how the agent works, provided all necessary access, and generated files with instructions, coding standards, and a high-level architectural diagram of service dependencies, we were able to establish smooth, uninterrupted operation.

Here’s what I recommend for those just starting on this path:

1. Define the goal and establish baseline metrics

Before starting a pilot, it’s important to have a clear understanding of why you need an agent: to reduce review time, automate tests, or decrease the number of bugs. Without KPIs, the team won’t be able to prove the agent’s value, and the project may end up “going nowhere”.

Create baseline metrics: average time per task, number of bugs in QA, percentage of repeat tasks. For example, this allowed us to measure the average time for code reviews and the number of corrections after the first review.

2. Integrate the agent into the workflow

The AI agent needs to live where the team works: GitHub, Jira, Slack, or the IDE — not in a separate “sandbox”. Otherwise, no one will use it in real releases, and its suggestions will become outdated.

I recommend connecting the agent to CI/CD (GitHub Actions, Jenkins, etc.) so it can create PRs, comment on builds, and respond to code events. At Waites, we did this gradually: Copilot Agent was integrated into GitHub for creating Pull Requests and embedded into the review pipeline. At first, the agent checked the results, and then the team lead validated them.

3. Teach people how to interact with the agent

An agent isn’t a magic button — it’s a tool that requires correct prompts and result verification. Without preparing the team, some people will ignore the agent, while others may overtrust it, leading to coding errors.

Conduct a short onboarding: teach developers to frame tasks as actions (“create a test,” “refactor this”) rather than questions. At Waites, we initially gave the agent time to “get used” to each developer’s style. As I mentioned earlier, Copilot Agent only started working effectively about a week after analyzing the project structure — DTOs, services, providers, and models. After this, team productivity noticeably increased, and testing and code reviews became much faster.

4. Ensure security and policies

Agents can inadvertently send internal data to external APIs or insert code snippets with incompatible licenses. To prevent data leaks or legal issues, create an internal AI policy. This should specify which data must never be entered into agents (keys, passwords, client data), how code is reviewed, and who is responsible for releases.

At Waites, we addressed this at the architectural level: all tools with code access run within the corporate environment (Gemini Enterprise, GitHub Copilot with API restrictions). For sensitive projects, we used separate isolated environments — similar to how we handled testing new databases — to avoid data leaks. Additionally, we follow information security principles per ISO/IEC 27001, meaning all outputs are always validated by a human.

5. Plan for scaling from the start

If the pilot succeeds, you need a plan to roll out the agent to other teams. Without it, the agent remains a “toy” for a single group, with no systemic impact.

I recommend creating an internal platform with prompt templates, integrations, and guides. Add features gradually — from testing to CI/CD and documentation.

Conclusion

Implementing AI agents isn’t about a “magic button”; it’s a systematic approach that turns chaos into efficiency. Our experience at Waites shows that with proper integration, training, and a focus on security, agents can significantly speed up work, reduce bugs, and free up time for generating new ideas. Start with a pilot, measure the results, and then scale. AI will become an even more powerful tool in the future, but remember: the key factor for success is the people managing these technologies. If your team is prepared, don’t hesitate — AI agents are already here, ready to help your business grow.