Kravos AI has officially launched its public beta, opening access to its managed infrastructure platform for autonomous AI agents. Designed to eliminate the backend engineering required to productionize AI, Kravos unites managed knowledge ingestion, retrieval, skills, external tools, and multi-modal voice and chat runtimes into a single execution layer. Developers can even build, ground, and test agents directly from coding assistants like Cursor and Claude via open Model Context Protocol (MCP) support. Builders can start creating for free at kravos.ai.
MAKATI CITY, PHILIPPINES – October 16, 2026 – Kravos AI today officially announced the public beta launch of its dedicated AI agent infrastructure platform, opening global access for developers, enterprises, and technical operators to build, deploy, and scale custom autonomous agents. Available immediately with a self-serve Starter tier, the platform eliminates the complex backend engineering required to operationalize AI, allowing teams to move from concept to production-grade deployment in minutes.
While foundation models provide raw reasoning, taking autonomous agents into production has historically required months of fragmented plumbing, including custom vector search pipelines, brittle tool connectors, and disparate frontend widgets. Kravos AI solves this by delivering the complete execution layer around the model: knowledge grounding, skill orchestration, tool execution, and deployment runtimes are connected out of the box.
“Foundation models have made intelligence accessible, but the engineering required to make agents reliably take action, search private data securely, and interact across channels remains a massive barrier for most organizations,” said Mr. Marc Dagatan, Co-Founder and CEO of Kravos AI. “Kravos AI is built to carry that infrastructure weight. Builders can configure complex business logic, connect their data, and even build directly from AI coding environments like Cursor and Claude using open standards like MCP—while our platform handles the runtime, security, and execution below.”
Key Capabilities of the Kravos AI Platform
The public beta introduces a comprehensive suite of developer and enterprise tools designed for the complete agent lifecycle:
Remote Model Context Protocol (MCP) Support: Developers can connect their preferred coding assistants (Cursor, VS Code, Claude, and ChatGPT) directly to Kravos AI via Streamable HTTP, enabling teams to scaffold, ingest data, and manage agent configurations programmatically from their development environments.
Isolated High-Performance RAG Architecture: Agents are grounded in proprietary enterprise knowledge—including live web crawls, internal documentation, and files—accelerated by a dedicated, isolated RAG cache that reduces retrieval latency to sub-10ms while preventing cross-agent data exposure.
Instant Multi-Modal Deployment: Completed agents can be deployed instantly using a single-line drop-in script tag supporting both real-time voice and text interfaces, or embedded directly into existing products via developer REST APIs.
Deterministic Tool Execution & Action Workflows: Beyond conversational Q&A, agents can execute real-world workflows, query live databases, trigger webhooks, and interact with external enterprise software using standardized tool definitions.
Live Observability & Human-in-the-Loop Takeover: Operators can inspect step-by-step agent reasoning traces, audit tool execution payloads, and intervene with seamless human handoff whenever conversations require human judgment.
Immediate Availability and Getting Started
The Kravos AI public beta is available immediately to developers, startup teams, and organizations worldwide. Users can create a free account to test sandbox agents, configure knowledge sources, and explore the developer API with no credit card required.
To sign up or explore technical documentation, visit https://kravos.ai.