AI-Driven Software Delivery

Slow or unreliable CI/CD pipelines can hinder product releases and competitiveness. We're addressing this challenge for clients using AI agents and LLMs to optimize software delivery processes.

Tech teams know the pain of slow or unreliable CI/CD pipelines. Delays here can mean pushing back product releases, losing time, and ultimately falling behind the competition. We’ve been working with several clients to tackle this issue head-on using the power of AI agents and LLMs.

Why it matters:

By optimizing your CI/CD pipeline, you’re looking at faster, more reliable software delivery. This translates to quicker time-to-market, which is a huge win for maintaining a competitive edge.

Pain Point:

No one wants to be stuck with a slow or error-prone pipeline. It holds up product releases and can seriously impact your team's productivity and your company's bottom line. We’re here to change that.

Generative AI can change the game:

We use specialized LLM agents that dig deep into code, logs, tech stack, and the unique standards your team follows. These AI agents don’t just spot existing issues; they predict potential ones before they happen. That means less downtime, smoother deployments, and happier dev teams. We've implemented this solution for several of our clients with excellent results.

Let’s think of a concrete example. Consider an agent specialized in identifying the following issues:

  • Unexpected errors or exceptions
  • Slow or stalled pipeline stages
  • Inconsistent or incorrect outputs
  • Deviations from expected metrics or thresholds

The LLM agent is also aware of the technical stack, environment parameters, and standards.

When the agent detects an issue, it generates an alert with a concise summary of the problem, including relevant context and potential root causes. The alert is sent to the appropriate teams or individuals for investigation and resolution. The agent can also provide recommendations for optimizing the pipeline, such as:

  • Adjusting resource allocations (e.g., CPU, memory, storage) for specific stages
  • Identifying and removing bottlenecks or inefficient steps
  • Suggesting improvements to the pipeline configuration or code
  • Recommending changes to the testing strategy or coverage

In some implementations, the agent learns from the feedback and actions taken by the teams in response to its alerts and recommendations. This allows it to refine its monitoring capabilities, provide more accurate and relevant insights, and proactively suggest optimizations based on historical patterns and best practices.

Moreover, the LLM agent can assist in other aspects of CI/CD pipeline management, such as:

  • Generating documentation and runbooks for pipeline setup and maintenance
  • Answering questions and providing guidance to pipeline operators and developers
  • Automating routine tasks like pipeline deployment, rollbacks, and scaling
  • Integrating with other tools and systems in the CI/CD ecosystem
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Charafeddine Mouzouni
Partner at Cohorte

08/2025