AI Automation: Deploying a Self-Learning Multi-Agent System via Yandex AI Studio

Automation Stack & Architecture

• Agent Framework: Yandex AI Studio (Multi-agent architecture / Agent API).
• Integration Layers: Agentic Memory Management + GitHub repository integration (`self_learning_skills`).
• Target Outcome: Implementation of autonomous agents capable of storing successful solutions to prevent redundant computation or incorrect pathfinding.

Agent Roles & Tools Assignment

• Yandex AI Architect Agency
  • Goal: Create voice interfaces, automation systems, and manage context/memory within specialized agent architectures.
  • Tools assigned: `Agentic API`, Contextual memory modules, Voice interface protocols.
• Self-Learning Module (Skill Integration)
  • Persona: Evaluator and Knowledge Retriever.
  • Goal: Detect if an agent has already solved a specific task; mark unsuccessful attempts as invalid paths; store proven methods for future sessions.
  • Tools assigned: Long-term storage (Memory), Decision validation logic via `self-learning skills`.

Step-by-Step Workflow Orchestration

1. Initialize connection through the Yandex AI Studio Agentic API requesting access to multi-agent coordination capabilities.
2. The system executes a wayfaring search (`search_for_solution`) but first queries the long-term memory layer populated by previous successful executions or failed attempt markers.
3. If no prior solution exists in current session data, the agent performs any requiredtask using general reasoning patterns provided by Yandex experts.
4. Upon completion of a decision/action path, use the get self-learning skillset code block to evaluate results.
5. Logic gate applied:
   — If result is correct: Mark approach as

! DYOR (Do Your Own Research)