Enhanced MCP Security, New Speech Service Integrations, and More with Cognigy.AI 2026.3

Enhanced MCP Security, New Speech Service Integrations, and More with Cognigy.AI 2026.3

7 min read

Authors name: Nhu Ho February 10, 2026

The 2026.3 release continues to give enterprise teams greater flexibility and control when building, securing, and optimizing AI Agents. Key highlights include expanded speech service integrations, enhanced authentication for MCP Tool access, and a new embedding model option for Knowledge AI.

Robust Authentication for MCP Tool Access

Security is further strengthened in this release with the introduction of OAuth 2.0 support for MCP Tools used in LLM Prompt and AI Agent Nodes. OAuth 2.0 replaces or complements static authentication methods like API keys with short-lived, token-based access, reducing the risk associated with long-lived credentials.

This enhancement helps protect integrations against unauthorized access and credential leakage, aligning with widely adopted enterprise security standards. It also supports stricter internal security requirements through controlled scopes, centralized credential management, and easier credential rotation, making AI Agents’ access to external tools both safer and more auditable.

Expanded Speech Service Flexibility

Cognigy.AI further expands its voice ecosystem with new speech service integrations, providing more options for voice quality, transcription accuracy, and language coverage across voice-enabled AI Agents.

Speechmatics is now available as a provider for both Speech-to-Text (STT) and Text-to-Speech (TTS), while ElevenLabs has been added as an additional STT provider. Additionally, ElevenLabs is now fully supported in Voice Preview and Interaction Panel Call, allowing you to test, compare, and fine-tune voice interactions directly within Cognigy.AI before rolling them out to production.

Native Gemini Embedding Model Integration

Cognigy.AI now natively integrates Google’s gemini-embedding-001 model for Knowledge AI, expanding the range of embedding models for knowledge ingestion. This gives you more flexibility to align embedding performance with your existing AI stack, cloud provider strategy, or vendor preferences, while catering to specific content types, languages, and levels of semantic complexity.

Other Improvements

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For further information, check out our complete Release Notes here.