From Assistants to Virtual Employees: How AI Agents Are Integrated into Corporate Processes

Technological advancement is transforming task allocation across IT teams: artificial intelligence is moving from executing point commands to solving complex task sequences. At the eighth MWS meetup for DevOps and SRE specialists, representatives from MTS Web Services and Orion Soft shared their experience integrating autonomous AI agents into engineering processes.

Incident Resolution Practice

One of the key use cases for agents is investigating technical incidents. The system connects to corporate knowledge bases, ticket archives, and issue trackers. When a ticket arrives, the neural network analyzes logs, finds similar historical incidents, and generates remediation options.

Current adoption results demonstrate the following performance metrics:

• 45% of tasks are resolved completely by the agent without specialist intervention;
• 40% of requests receive a partial solution—the system prepares context and proposes actionable options, speeding up the engineer's workflow;
• 15% of cases require a complete review due to model inaccuracies or hallucinations.

At the same time, the agent is not granted permissions to make critical changes directly: it drafts an action plan and pull requests, while a human approves them.

The Main Barrier Is Context, Not Code

Modern language models generate code rapidly; however, overall release velocity is constrained by context. Without knowledge of internal limitations, architectural decisions, and infrastructure history, an agent can make critical errors (for example, exposing internal services to the public internet).

For a virtual employee to function correctly, restructuring how corporate knowledge is managed is crucial:

• Documentation as infrastructure: architectural diagrams, APIs, and guidelines must be updated in lockstep with the codebase;
• Single source of truth: humans and AI agents must operate using the exact same up-to-date instructions;
• Relevance monitoring: specialized agents can automatically track discrepancies between the codebase and service descriptions.

Shifting the Engineer's Role: The Transition to Orchestration

Engineers are not becoming obsolete—their focus is shifting toward architectural oversight and risk management. AI takes on routine analysis by identifying deadlocks, race conditions, and architectural inconsistencies, while the specialist makes the final decisions.

Experts emphasize that attempting to build a single all-purpose agent for the entire development lifecycle is impractical. A pragmatic approach relies on role specialization: distinct agents are built for incident monitoring, code review, or documentation updates, each operating with strictly scoped access permissions.