Corvic AICorvic AI

Agent Harness

Build the agent. Swap the model. Keep the work.

The harness is what runs under every playbook, app and pipeline in Corvic. You choose the model, connect tools over MCP, and ground the agent in the data in your room — without writing a line of code.

Everything in, everything out

Any model, any connector, and data in whatever shape it arrives — into one configuration. Out comes the chat, the app, the playbook and the pipeline your team actually uses.

Your stack

Models

  • OpenAI
  • Anthropic
  • Google
  • xAI
  • Open source

Or your own endpoint

Connectors

  • SalesforceSalesforce
  • SlackSlack
  • StripeStripe
  • GitHubGitHub
  • NotionNotion

+38 more over MCP

Your data

  • SnowflakeSnowflake
  • DatabricksDatabricks
  • AWS S3AWS S3
  • PDFs & scans
  • Audio & images

No modelling first

The agent harness

Model

Any provider

Swapped in config, not code

Tools

Over MCP

Read and write where work happens

Grounding

Your own data

Answers cite the row they came from

Access

Existing permissions

Scoped per person, per source

What you get

ChatExplore your data, with citationsKnowledge graphsHow things connect, and what they meanPlaybooksA good run, repeated on a scheduleData pipelinesThe tables underneath, kept currentAgentic data processingWhole document piles into clean rowsLive web appsPublished to a URL your team opens

No pipelines to build. Configure the harness once — swap the model, add a connector, tighten a permission, and everything above keeps working.

What the harness gives an agent

The parts a team ends up building by hand — model choice, tools, grounding, permissions — already assembled and swappable.

  • Configure, don't code

    Model, prompt, tools and knowledge on one screen. No framework to learn.

  • Every model, no lock-in

    OpenAI, Anthropic, Google, xAI, open source — or your own endpoint.

  • Compare, then promote

    Run one task across models, weigh quality against latency and cost.

  • Tools over MCP

    Web search, warehouse queries, files — called with your credentials.

  • Grounded in your room

    Answers come from the tables and documents you attached, with citations.

  • Scoped by permission

    A room bounds what an agent can read. Access follows the person asking.

How an agent gets built

  1. 1

    Open a room

    Attach the tables, files and connectors this agent is allowed to see.

  2. 2

    Pick a model and tools

    Start on a default, add MCP servers, then swap models whenever you like.

  3. 3

    Put the agent to work

    Chat with it, save the run as a playbook, or let it build an app.

Try it yourself

A working replica of a Corvic room, on invented data. Switch rooms, change the model, open the tabs — then take your own question to the real thing.

Revenue Analytics›

Sketch out your next data app.

Describe what you want to build

System PromptTools · 1Skills

Point an agent at your own data

Open a room, connect a source, and ask it something only your data can answer. Free to start, no credit card.