Agentic Framework

Agents for high-stakes workflows that can't fail

Byg agenter til workflows hvor fejl ikke er en mulighed. Giv dem værktøjsadgang, sikkerhedsgrænser og et fuldt audit trail, ikke bare en prompt.

  • Grounded answers

    Agents retrieve from trusted sources, not internal knowledge. Registry connectors for pre-built capabilities, MCP for tool servers, A2A for agent-to-agent messaging.

  • Safe execution

    Typed inputs, explicit tool schemas, and guardrails around every action. Agents can pause for human approval and resume only on explicit consent.

  • Full auditability

    Replayable traces, structured logs, and OpenInference export. Every decision, tool call, and context change is observable.

  • Persistent contexts

    Multi-turn conversations with strict isolation between contexts. No cross-context leakage, no shared-state spaghetti.

LLMs are not production-ready on their own

They can't reach the data they need at runtime, so they guess. They can't act safely on external systems, so they stall. The Agentic Framework fills both gaps: agents look up authoritative data through connectors, and execute multi-step workflows inside boundaries you set.

How it works

Plan, execute, and trace agent workflows

  • Agent runtime

    Receives requests, plans steps, selects and invokes connectors, returns results as tasks or messages. Contexts carry state across turns.

  • Connectors

    Five types. Registry for pre-built capabilities, MCP for external tool servers, Agent for nested agents, A2A for remote agents, Schema for custom tools.

  • Contexts & traces

    Conversation state with strict isolation between contexts. OpenInference trace export for debugging and observability.

User-facing assistants

Agents that answer questions, edit documents, and drive admin workflows, connected to your knowledge base instead of guessing from training data.

Hvad er status på de tre åbne GDPR-klager fra sidste uge?

GDPR-complaints-2025-09.pdf
Søgte i ZendeskTjekkede NotionKrydstjekkede policy

Automated pipelines

Trigger agents by API or event to process records, generate reports, and update downstream systems. Monitor every step with tasks and traces.

POST /agents/tasks2,400 records
Valideret via DocuSignTjekkede consent-registry

Customer-embedded agents

Let customers bring their own tools and systems. Agents combine Corti capabilities with third-party and customer-owned services through typed connectors.

Hent vores Q3-incidentrapport fra det interne SOX-dashboard.

Forbundet til NotionHentet fra SnowflakeMarkeret 2 unormale værdier

Compliance and audit workflows

Agents that can check policies, enforce rules, and log every action. Full traceability from request to output, nothing falls through cracks.

Opfylder den foreslåede vendor-fornyelse vores data retention-policy?

Parset med IroncladTjekkede retention-policyLogget til audit-trail

Samme stak ned til GPU'en

Peg agenter mod Corti Models når I har brug for EU-hostet, OpenAI-kompatibel inferens, eller kør Agentic API i det EU- eller US-miljø jeres projekt bruger. Workflow-design er jeres; infrastrukturen er vores.

  • Skalér agenter uden at ombygge modellaget
  • Usage og traces tilgængelige per agent og context
  • EU- eller US-projektmiljøer, samme platformmønstre
agentic
const myAgent = await client.agents.create({  name: "My First Agent",  description: "A simple agent to get started with the Corti Agentic Framework",  lifecycle: "persistent",  connectors: [    { type: "registry", name: "memory-expert" },  ],});

A2A er hvordan jeres applikation taler med Corti-agenter: beskeder, tasks, streaming. MCP er én af fem connector-typer: hvordan en agent kalder eksterne tool-servers. De komplementerer hinanden på forskellige lag.

Det er det centrale reasoning-lag. Det modtager requests, planlægger arbejde, vælger og kalder connectors, og returnerer tasks eller messages, med contexts til multi-turn kontinuitet.

Agentic API er tilgængeligt i EU- og US-miljøer (api.eu.corti.app / api.us.corti.app). Par det med Corti Models i den matchende region, inkl. EU sovereign hosting for EU-projekter.

Quick operations like classifications come back as messages. Longer workflows that coordinate multiple connectors or wait on downstream systems become tasks you can monitor, with artifacts for the output.

Text parts are for instructions the agent reasons about directly. Data parts carry structured JSON that gets stored in context memory and retrieved when needed. You can use both in one message.

No. Contexts provide strict data isolation. If you need to share information across contexts, pass it explicitly via data parts in your messages.

It analyzes the request and reasons about what connectors are needed. You can guide selection by writing system prompts in the agent configuration. The runtime can chain connectors in sequence or parallel.

Agentic Framework

Ship agenter I kan auditere
og skalere uden kaos

Opret en agent fra API'et, eller tal connectors og deployment igennem med teamet der driver stakken.