Kravos AI launches public beta for enterprise agent infrastructure

Bridging the gap to production

Kravos AI recently launched the public beta of its managed infrastructure platform for autonomous AI agents. While foundation models provide the basic reasoning for AI, turning those models into reliable tools for a business is often difficult. Companies frequently face a hurdle where they must build months of custom software to make an agent work correctly.

The platform is designed to eliminate the backend engineering required to operationalize these systems. Instead of building custom vector search pipelines or brittle tool connectors, developers can use Kravos AI as a complete execution layer. This allows teams to move from an initial concept to a production-grade deployment much faster than traditional methods.

According to Dugout Philippines, the platform aims to solve the problem of fragmented plumbing in AI development. By providing a unified system for knowledge grounding and skill orchestration, Kravos AI seeks to make it easier for organizations to deploy agents that can reliably take actions. Marc Dagatan, Co-Founder and CEO of Kravos AI, noted that the engineering required to make agents interact across channels remains a massive barrier for most organizations.

How the infrastructure works

The platform functions by uniting several complex components into one managed environment. This includes knowledge ingestion, retrieval, and multi-modal runtimes for both voice and chat. By handling these elements in a single layer, the platform ensures that the AI agent has a consistent way to access information and interact with users.

One specific technical feature is the isolated High-Performance RAG architecture. RAG, or Retrieval-Augmented Generation, is a method where an AI looks up specific information from a provided dataset before answering a prompt. Kravos AI uses a proprietary cache that reduces retrieval latency to sub-10ms while ensuring that data from different agents remains isolated and secure.

The system also supports deterministic tool execution. This means the AI can follow specific workflows to query live databases, trigger webhooks, or interact with other enterprise software. These actions are governed by standardized tool definitions, which help ensure the agent performs the correct steps every time it is asked to complete a task.

Developer tools and integration

Kravos AI is making it easier for developers to build agents within their existing workflows. The platform supports the open Model Context Protocol, or MCP, which allows developers to connect coding assistants like Cursor, VS Code, Claude, and ChatGPT directly to the infrastructure. This enables teams to manage agent configurations and ingest data programmatically from their preferred development environments.

Once an agent is built, the platform offers several ways to deploy it to the end user. Developers can use a single-line drop-in script tag to provide real-time voice and text interfaces. Alternatively, for those who want to integrate the AI into an existing application, the platform provides developer REST APIs for deeper customization.

The platform also includes features for live observability and human-in-the-loop takeover. This allows human operators to inspect the step-by-step reasoning traces of an agent as it works. If a conversation reaches a point that requires human judgment, the system allows for a seamless handoff from the AI to a person.

Availability and next steps

The public beta is currently available to developers, startups, and large organizations worldwide. Users can sign up for a free account at kravos.ai to begin testing sandbox agents. This free tier allows users to explore the developer API and configure knowledge sources without needing to provide a credit card upfront.

Kravos AI was established in 2026 to bridge the gap between raw AI models and reliable business workflows. The company positions itself as a zero-infrastructure platform rather than a service agency. This means they provide the tools for teams to construct and manage their own custom agents tailored to their specific requirements.

Moving forward, the platform aims to serve as a central control plane for modern AI operations. By providing built-in step auditing and enterprise-grade security, it targets organizations that need to move beyond simple chat interfaces. The goal is to provide a scalable way for companies to deploy agents that can handle complex, multi-step business logic.