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 15, 2026 – Kravos AI today officially announced the public rollout of its dedicated AI agent infrastructure platform, providing developers, product teams, and enterprises with a unified execution environment to build, deploy, and scale custom autonomous AI agents. Following an exclusive industry preview in August 2026, the company has opened platform access at kravos.ai, removing the backend complexity that stalls over 70% of enterprise AI agent deployments.
What is Kravos AI? Kravos AI is an infrastructure software platform and control plane for autonomous artificial intelligence agents. While foundation model providers (such as OpenAI, Anthropic, and Google) focus on core LLM intelligence, Kravos AI provides the execution stack surrounding the model. The platform unifies knowledge ingestion, isolated RAG retrieval, skill definitions, tool integrations via the open Model Context Protocol (MCP), and multi-channel deployment into a zero-maintenance runtime.
Addressing the Production Agent Execution Gap Building reliable, task-executing AI agents typically forces engineering teams to build custom infrastructure from scratch—stitching together vector stores, embedding APIs, complex auth logic, and brittle tool connectors. Kravos AI replaces this fragmented plumbing with a production-ready environment that takes agents from concept to live deployment in minutes.
The platform provides a complete lifecycle for autonomous agent engineering:
Proprietary Isolated RAG Caching: Grounds agents in proprietary enterprise data (web crawls, PDFs, documentation, and structured files). By caching finalized retrieval context at the agent and data source level, Kravos slashes knowledge retrieval latency to sub-10ms and eliminates redundant vector database queries.
Remote Model Context Protocol (MCP) Support: Developers can connect modern AI coding environments (including Cursor, VS Code, Claude, and ChatGPT) directly to Kravos via streamable HTTP to scaffold, test, and manage agents programmatically.
Turnkey Multi-Modal Deployment: Agents deploy instantly via a single-line tag delivering embeddable voice and chat interfaces, or directly into existing business applications via developer REST APIs.
Deterministic Tool Execution & Workflows: Allows agents to safely trigger real-world actions, call external software APIs, and query live production databases with built-in idempotency and transactional rollback protections.
Real-Time Observability & Human Fallback: Built-in tracing monitors step-by-step agent reasoning, tool call parameters, and accuracy guardrails, backed by a human takeover dashboard for seamless live escalation.
“Working on something quietly for months and then finally unveiling it to the builder community is an incredible milestone,” said Marc Dagatan, Chief Executive Officer of Kravos AI. “Foundational models have solved general reasoning, but the engineering required to run agents reliably in production is where teams get stuck. We don’t train base models, we build the platform that runs your agents. Kravos provides the entire operational stack around the model, including infrastructure, governance, and deployment, so teams can focus 100% on their core workflow logic.”
Immediate Availability and Free Access Early access to the Kravos AI platform is open immediately to developers, technical founders, and enterprise teams globally. Users can start building sandbox agents with no credit card required on the platform’s self-serve Starter tier.
To begin building custom AI agents or explore platform documentation, visit www.kravos.ai
Frequently Asked Questions (FAQ)
What is Kravos AI? Kravos AI is an enterprise-grade execution and control platform that hosts, coordinates, and scales autonomous AI agents, eliminating the need for teams to build in-house AI infrastructure.
How does Kravos AI differ from foundation model providers? Foundation models provide raw LLM reasoning capabilities. Kravos AI provides the operational infrastructure around those models, including data ingestion, proprietary isolated RAG caching, tool execution via MCP, human-in-the-loop controls, and frontend deployment runtimes.
Can developers build Kravos agents from AI coding assistants like Cursor? Yes. Kravos AI supports Remote Model Context Protocol (MCP) over Streamable HTTP, allowing developers to configure, test, and trigger document ingestion for their agents directly inside environments like Cursor, VS Code, Claude, and ChatGPT.