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The Corti Agentic Framework is a modular AI system for building advanced agents that perform clinical and operational tasks without months of complex architecture work. In healthcare, an LLM agent is not a chatbot answering everything from internal knowledge. The language model is used for reasoning and planning: understanding a request, breaking it down, and deciding which tools and data sources are best suited to handle each part.
The Agentic Framework supports use cases across the healthcare spectrum, from chat-based assistants for doctors to automating EHR data entry and powering clinical decision support workflows.

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What problems it solves

Modern LLMs are powerful, but on their own they are insufficient and unsafe for clinical use. The Agentic Framework addresses two fundamental gaps:

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LLMs do not have reliable access to clinical data

LLMs cannot be trusted to rely on internal knowledge alone. In healthcare, responses must be grounded in clinically validated reference sources, real-time patient and system data, and customer-owned systems and APIs. Without access to these sources at runtime, models are forced to infer or guess, which is unacceptable in clinical settings. The Agentic Framework addresses this by enabling agents to retrieve information directly from trusted external tools through connectors. Instead of hallucinating answers, agents look things up, verify context, and base their outputs on authoritative data.

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LLMs cannot safely act on the world

Clinical workflows require more than generating text. They involve interacting with real systems: querying EHRs, drafting and updating documentation, preparing prescriptions, and triggering downstream processes. The framework provides a controlled execution layer that allows agents to plan actions, invoke tools, and coordinate multi-step workflows while remaining within clearly defined safety boundaries. Where necessary, agents can pause execution, request human approval, and resume only once explicit consent is given.

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What you can build with it

Using the Agentic Framework, teams can build:
  • Clinician-facing assistants: documentation editing, guideline and reference lookup, coding and administrative support
  • Programmatic agent endpoints: embedded into existing clinical software, triggered by events, APIs, or workflows
  • Customer-embedded agents: customers bring their own tools and systems; agents combine Corti, third-party, and customer capabilities
All of these share the same underlying agent runtime, safety model, and connector layer.

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Built for healthcare by design

Healthcare is not a general-purpose domain, and the framework reflects that reality.

Safety first

Typed inputs and outputs, explicit tool schemas, and guardrails around action-taking ensure safe operation in clinical environments.

Auditability

Every decision and tool call is observable with replayable traces and structured logs for transparency, compliance, and quality assurance.

Domain-specific reasoning

Fine-tuned reasoning layers optimized for healthcare language, workflows, and compliance needs.

Connector-based architecture

A unified connector model enables agents to call registry tools, MCP servers, other agents, remote A2A agents, and custom schema tools.

Memory and context management

Maintain persistent, context-aware conversations and manage multiple active contexts without losing information throughout the session.

Registry connectors

Access a library of pre-built connectors maintained by Corti and partners: specialized tools that connect to data sources and services to execute clinical and operational tasks.

Third-party integrations

Plug directly into EHRs, clinical decision support systems, and medical knowledge bases with minimal setup.

Run-time context

Pass relevant context with each query, including structured data formats, enabling connectors to work with rich, domain-specific information.

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v2 at a glance

The v2 API introduces several key changes:
  • Unified connectors: Experts, MCP servers, and sub-agents are all replaced by a single connectors model with five types: registry, mcp, agent, a2a, and schema
  • Clean CRUD verbs: Consistent REST patterns for agents, connectors, contexts, and feedback
  • A2A v1.0 only: The A2A protocol is upgraded to v1.0; v0.3 is deprecated but still available on the v1 API surface
  • First-class metadata: Agents have visibility (private, unlisted, public), lifecycle (ephemeral, persistent), model, and labels
  • New endpoints: Contexts, connectors, registry, usage, artifacts, feedback, traces, and agent cards all have dedicated API surfaces
  • Prefixed UUIDv7 IDs: All resource IDs use type-prefixed UUIDv7 format (e.g. agt., ctx., task., msg., con.)
Migrating from v1? See the v1-to-v2 migration guide for a detailed mapping of changes.

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Who it’s for

The Agentic Framework is built for teams working on healthcare software:
  • Healthcare software companies embedding intelligent automation directly into their products
  • Enterprise customers building internal, AI-powered clinical workflows
  • Advanced engineering teams that need flexibility, control, and strong safety guarantees without building bespoke agent infrastructure from scratch
The platform is designed to make it easy to go from demo to production-grade clinical AI systems that operate safely in real-world healthcare environments.

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Next steps

Quickstart

Create your first agent and send a message in minutes.

Core concepts

Learn the fundamental building blocks of the Agentic Framework.