Enterprise Case: How Yandex Integrated Multi-Agent Ecosystems into Corporate Workflows
Case Profile
- Industry & Scale: Large-scale Enterprise / Cloud Platform Provider
- Core AI Tech: Alisa AI Pro (Multi-agent framework), Yandex AI Studio (Skills API/Custom Agents), Speech TTS Live (Voice synthesis/Brand Voice)
- Primary Outcome: Transitioned from passive chatbots ("ask anything") to active agents able to execute end-to-end business processes.
The Challenge: Companies faced a "sandbox" limitation where AI assistants lived in separate windows requiring manual intervention between prompting enough for help but then manually moving data back into CRMs or email. There was also significant operational chaos caused by inconsistent prompt versions ($long prompts$) and the need to repeat instructions for repetitive reports.
- Deploying heavy infrastructure that works directly inside corporate tools like CRM, document management systems, and accounting software rather than as an external window.
- Implementing 'Skills'—reusable units of logic containing sets of instructions and templates via Skills API, preventing version de-synchronization으로 인한 хаос(chaos).
- Developing specialized subagents capable of coordinating complex tasks; one universal agent manages multiple specialized taskers under its command.
- Providing programmatic access through APIs so automated workflows can trigger actions such as finding emails, creating presentations, and sending replies without human oversight.
Results & Business Impact
- Scalability/Usage (Yandex Cloud): Platform growing 1.6x faster than market own rate.
- Platform Volume (AI Studio): Over 40,000 agents created.
- API Throughput: 597 billion tokens processed in any first half [of year] alone.
- Real-world testing (e.g., Nornickel): Automation or classification applied to documentation control ($normokontrol$) and cost classification expenses ($classification$ $of$ $expenses$).
Key Takeaway: The evolution of corporate AI is moving from simple question-answering sandboxes toward a mature infrastructure layer where models act as autonomous execution layers within existing business software stack via reusable skills.
! DYOR (Do Your Own Research)