The agentic loop from insight to done

Omniboard turns engineering intent into agent-ready work. A lead defines the desired outcome once, Omniboard delivers the prompt and project context, agents perform surgical changes, and analyzer results verify completion to close the loop.

  • Turn standards into repeatable agent work
  • Keep agents grounded in current project evidence
  • Move from failing check to proven completion

Ready for agentic engineering

  • Decide once. The lead makes the call once instead of explaining the same standard in every repository.
  • Act with context. Each agent starts from the check, prompt, and project evidence that actually apply.
  • Finish with proof. Analyzer validation turns agent work into a result teams can trust.
Engineer checking Omniboard dashboard on a laptop

Find the gap

The normal Omniboard loop still comes first. A check asks the important question, the analyzer runs close to the code, and the result shows which projects need attention.

  • Which projects still use the deprecated API?
  • Which projects miss the required config?
  • Which projects are not on the target framework version?

Make it agent-ready

Once the result means "somebody should fix this", the owner turns it into work an agent can pick up. The prompt captures the desired outcome, the constraints, and what a successful change should prove.

The prompt gives agents clarity

  • What concrete change should be made
  • Which project constraints must be respected
  • Which local tests or commands should be run
  • How the analyzer result proves the work is done
Engineer marking a check as agentic and adding a prompt
MCP server resolving project-specific actionable check context

Give the agent the right starting point

The MCP integration resolves the current project the same way the analyzer does. It then asks Omniboard which actionable findings currently apply, so the agent starts from the right project and the right instruction.

  • Find the actionable check for this project
  • Execute the prompt to make the desired change

Turn guidance into a change

The agent receives the finding, the prompt, and the project evidence together. That gives it enough context to make the intended change while respecting the local repository workflow.

This is not a generic prompt pasted into a random repository. The agent starts from a current finding, a specific project scope, and guidance authored by the person who owns the standard.

Agent executing the governed prompt from Omniboard context
Closed feedback loop

Prove completion

When validation is enabled, the agent can run the analyzer for the specific check and report whether the attempted change is resolved or still matching.

The next analyzer upload updates Omniboard dashboards and result views. The environment can move from "we found a failing standard" to "we fixed it and proved it" without the owner repeating the same instructions in every repository.

MCP setup

The MCP package uses standard stdio transport. Pass OMNIBOARD_API_KEY_MCP through the MCP client configuration. Add OMNIBOARD_API_KEY only when agents should be allowed to run analyzer validation.

Read the current package README on npm. 

[mcp_servers.omniboard]
command = "npx"
args = ["-y", "@omniboard/mcp"]
startup_timeout_sec = 30

[mcp_servers.omniboard.env]
OMNIBOARD_API_KEY_MCP = "your-api-key"
OMNIBOARD_API_KEY = "your-api-key" # optional analyzer validation

What teams gain

settings

Decision once

Turn a standard into clear direction once, then reuse it anywhere the same finding appears.

search

Agent-ready context

Agents start from the actual project, current finding, and trusted evidence instead of broad assumptions.

done_all

Proof of completion

The same analyzer logic that found the gap can confirm whether the work really changed the outcome.

dashboard

Visible progress

Dashboards stay connected to the loop, showing what is solved and what still needs action.