Modern companies work with a whole suite of tools: task management systems, Git platforms, continuous integration tools, and cloud infrastructure. All of this interacts, and the larger the project, the harder it is to manage. The startup Overcut decided to create an agent system that could take over some of the routine tasks in such an environment – and does so on the Azure platform.
What Problem Overcut Solves
In short: their product helps automate typical tasks in the development process. We're not talking about simple scripts, but AI-based agents that know how to work with different systems simultaneously. For example, closing a «ticket» in Jira, updating documentation in Confluence, running a build in a CI system, or checking infrastructure status in the cloud.
The key feature is security and readiness for use in large organizations. Usually, in a corporate environment, any automation goes through a long approval process because you need to guarantee that data won't leak, the system won't break anything critical, and everything complies with company policy. Overcut designed its platform with these requirements in mind from the start.
Why Overcut Chose Azure
Why They Chose Azure
Azure provides not just cloud capacity, but a whole ecosystem of services that can be used to build agent systems. Firstly, there is the Azure OpenAI Service – access to GPT models through Microsoft's corporate infrastructure, with all necessary privacy guarantees and compliance with standards.
Secondly, integration tools: Azure Active Directory for access management, Azure Key Vault for storing confidential data, and Azure Monitor for tracking system performance. All of this is ready-made and doesn't require development from scratch.
This is important for a startup: you can focus on agent logic and their ability to solve real tasks rather than spending months building basic infrastructure.
How Agent Workflows Are Structured
In this case, an agent is a program that understands a request in natural language, plans a sequence of actions, and executes them via the APIs of various systems. For example, a developer might write: «Prepare release 2.3, update the changelog, and create a merge request». The agent parses this phrase, determines the steps, checks the current state of the codebase, makes changes, and creates a «pull request».
The difficulty lies in the fact that every company uses its own set of tools and processes. Some do all their work via GitHub and CircleCI, others use GitLab and Jenkins, and a third group uses proprietary internal systems. The platform must be sufficiently flexible to adapt to any configuration.
Overcut solves this through a modular architecture: there is a basic agent engine, and on top of it – a set of connectors to different systems. Each connector understands the API of a specific tool and can perform actions within it. The agent orchestrates the entire chain.
Security as a Foundation of Agent Systems
🔐 Security as the Foundation
In a corporate environment, you can't just give an agent access to all systems and hope for the best. Control mechanisms are needed: who can run agents, what actions are allowed, and how all operations are logged.
Overcut uses a role-based access model integrated with Azure AD. This means the agent's rights depend on the rights of the user who launched it. If you don't have access to production infrastructure, the agent won't be able to get in there either.
All tokens and keys are stored in Azure Key Vault, not in code or configuration files. Every action of the agent is recorded in logs, and you can track who did what and when. This is critical for audits.
Scalability and Performance
When an agent system works in a small team of ten people – that's one thing. But if a company has thousands of developers, the load increases drastically. You need to process many requests simultaneously without creating bottlenecks.
Azure provides tools for autoscaling: if the load grows, the platform spins up additional instances itself. If it drops – it turns off the extra ones, saving resources. For Overcut, this means their clients don't need to worry about whether the system will withstand peak loads.
Another point is latency. If an agent thinks for a long time or executes actions slowly, it's annoying. Therefore, optimization is important: caching intermediate results, parallel execution of independent tasks, and fast responses from language models via Azure OpenAI.
Practical Benefits of Agent Systems
What This Yields in Practice
For development teams, the main value is freeing up time. Routine tasks like updating dependencies, synchronizing documentation, or preparing releases take hours per week. Agents take this upon themselves.
For project managers – process transparency. You can see what the agent did, what decisions it took, and where errors occurred. This helps find bottlenecks in workflows.
For security teams – confidence that automation isn't creating «holes». Everything works within existing policies, all actions are controlled, and all data remains inside the corporate infrastructure.
Limitations and Open Questions for Agent Systems
Limitations and Open Questions
Agent systems are still evolving, and not all tasks can be automated equally well. If a process is clear and repeatable – the agent will cope. But if complex decision-making is required where context and experience matter, humans are currently irreplaceable.
Another issue is trust. Teams need to get used to the fact that part of the work is done by AI. This requires time and gradual implementation: first, agents perform simple tasks, then – more complex ones.
The question of cost also remains open. Calls to language models cost money, especially if there are many requests. A balance must be found between automation and infrastructure expenses.
Conclusions
Overcut shows how one can build agent systems for real corporate use. The key here isn't just in the power of AI, but in how well the platform integrates with existing tools and meets security requirements.
In this case, Azure acts not only as cloud infrastructure but also as a set of ready-made solutions: from access management to monitoring. This allows the startup to focus on its task rather than creating basic components.
Agent workflows are a direction that will continue to develop. The better models understand context, the more tasks they will be able to solve automatically. But for mass adoption, we need not just smart algorithms, but a reliable, secure platform. Overcut is following exactly this path.