ATLAS is a sophisticated platform designed to overcome common challenges in deploying multiple AI agents, such as framework silos, lack of semantic discovery, absence of cross-agent auditing, and insufficient human oversight. It establishes an infrastructure layer that enables autonomous agents to operate interoperably, be auditable, and ready for production environments. The platform achieves this through a structured pipeline that extracts user intent, checks for human-in-the-loop requirements, semantically routes tasks to appropriate agents, synthesizes responses, and immutably records every action in a tamper-proof SHA-256 audit chain for compliance with regulations like the EU AI Act Article 9. This ensures robust, verifiable, and controlled multi-agent operations without hardcoded rules or monthly infrastructure costs.
Key Features
01A2A Protocol v0.3 for seamless cross-framework agent interoperability.
02SHA-256 Audit Chain ensuring tamper-proof record-keeping and EU AI Act compliance.
03Shared tool access and registration using the Model Context Protocol (MCP).
04Human-in-the-Loop (HITL) workflow for critical action approval/rejection.
05Semantic routing via Qdrant for dynamic, intent-based agent discovery.
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