Agent console
Suggestions, evidence, action proposals, and approvals appear inside the operator console, on the same screen your team uses today. There is no separate AI dashboard, no chat thread to check, and no second audit trail to maintain.
Four surfaces, one workspace
Every agent interaction surfaces through one of four built-in components designed for the operator console. None require a separate tool, separate login, or separate training cycle.
Opens in the shell sidebar and reads your current view: the entities on screen, the filters you have set, and the recent receipts in the audit trail. Answers and suggestions are grounded in what the agent actually sees, not a generic model reply.
Three tabs: what the agent sees (context), a question surface, and the tools available in the current scope.
A right-side panel on every major module page. When an agent has a suggestion, it appears here as a structured proposal, not in a separate chat thread. Operators review, preview, approve, or dismiss without leaving the page.
The rail is driven by the same policy and approval boundary as every other action surface. A suggestion with too high a risk class for the current operator simply does not appear.
The safe bridge between an agent suggestion and a real change. Each card shows what will change, the expected impact, and whether a named person needs to approve before the command runs. Nothing moves until the operator confirms.
Cards walk a fixed lifecycle: propose, preview, approve (when required), execute. Decline and cancel are always available off-ramps.
Attached to every proposal: the records the agent read, how fresh they were, the relevant receipts and events, the policy decision, and which model and context version produced the suggestion. Operators know what the agent saw before they act on it.
Includes context hash, prompt template version, and freshness cursor so the suggestion is reproducible and auditable.
How a suggestion becomes a receipt
Every agent suggestion follows the same path. The operator is in control at every step that matters. Nothing runs without their confirmation, and the receipt records every decision along the way.
The agent reads the current view — entities, filters, recent receipts — and builds context.
A structured proposal card appears in the action rail, not a chat message.
The operator sees the expected impact before anything is written.
High-risk changes wait for a named operator. The approval is part of the receipt.
The command runs through the same governed path any other action uses.
The receipt, event, timeline, and evidence drawer stay queryable.
What you do not have to build
KarmanFlow's agentic UI layer is a first-party framework, not a bolt-on. Every module page gets the same surfaces through the same component contract. There is no per-module AI wiring.
Suggestions appear inside the screens your team already works from. There is no chat product to train operators on and no AI dashboard to maintain alongside the real console.
Approval gates are set by risk class and module, not per-agent prompt instructions. An agent that proposes a high-risk change will always pause for a named human, without any extra configuration.
The receipt from an agent-proposed action is the same shape as the receipt from an operator-run action. Your compliance team does not need a second read path for 'the AI stuff'.
An agent surface your operators trust is one that tells them what it saw, shows them what will change, and asks before it moves anything. Those are not UX features. They are the boundary.The KarmanFlow team
Ready to see it
We will stand up a preview workspace, run the Day-Zero seed, and show you the first agent proposal inside the operator console before you commit to anything.
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