Active workflows
4738 in productionProduct / control plane
One control plane.
Every AI agent accounted for.
Inventory what exists, put policy and human review in the path of sensitive work, and keep every decision, cost, and outcome attached to the run.
Product previewIllustrative data
AnthropicOpenAI
Mistral AI
Hugging FaceGoogle VertexAWS BedrockAzure AI FoundryLangChain
Pinecone
Datadog
Mistral AIHugging FaceGoogle VertexAWS BedrockAzure AI FoundryLangChainPineconeDatadogSolution / govern the whole agent surface
Move agents from pilot to production without losing control of what they can do.
Product previewIllustrative data
K Reviewed
Agent inventory
Ownership and control coverage
| Agent | Owner | Review path | Scope |
|---|---|---|---|
| Claims triage | Operations | Human review | 3 tools |
| Invoice match | Finance | Policy gated | 2 tools |
| Support routing | Customer care | Monitored | 4 tools |
Owner
Named for every agent
Access
Tools and data mapped
Review
Escalation path attached
01 / Inventory
See the agent surface before it becomes a blind spot.
Bring agents, owners, models, tools, data sources, environments, and review paths into one operating view. Start with what exists, then make the gaps visible.
- Named ownership
- Access boundaries
- Operating environment
Product previewIllustrative data
Action waiting for review
A policy checkpoint paused the tool write before execution.
Policy
Customer data handling
Action
Update system of record
Context
Prompt and retrieved sources
Decision
Reviewer rationale required
02 / Govern
Put policy and human judgment in the path of risky work.
Bind controls to the run itself. Sensitive actions can pause with the prompt, context, intended write, and policy result visible before a reviewer decides.
- Policy checkpoints
- Human review
- Exception rationale
Product previewIllustrative data
Run evidence / reviewable trace
- 01
Trigger received
Source and initiating identity recorded
- 02
Policy evaluated
Matched rule and result kept with the run
- 03
Human reviewed
Decision and rationale attached
- 04
Tool action completed
Outcome and destination recorded
03 / Evidence
Keep the evidence with the action.
Prompts, retrieved context, tool calls, approvals, exceptions, spend, and outcomes stay together so teams can answer what happened without rebuilding the story from scattered logs.
- Decision trail
- Review record
- Exportable evidence
Product previewIllustrative data
Reasoning
Models
Execution
Agent frameworks
Context
Knowledge
KommitControl plane
Actions
Business tools
Environment
Repos + cloud
Evidence
Audit exports
Keep ownership, policy, cost, and review attached as work crosses systems.
04 / Models + tools
Connect the stack without losing the boundary.
Kommit sits above the systems doing the work. Teams can connect models, agent frameworks, repositories, business tools, and cloud environments while keeping governance consistent.
- Provider choice
- Tool boundaries
- Cross-system record
PromptPolicyContextApproval
One run / one record
Evidence travels with the work.
The action, the rule that allowed it, the context behind it, and the person who reviewed it stay connected from trigger to outcome.
PromptPolicyContextApprovalOutcome
How it works
From unknown agent sprawl to governed operations in three clear moves.
01Inventory the agent surface
Identify active agents, owners, models, tools, data sources, environments, and unmanaged access. Start with a truthful map of what exists today.
02Bind policy and review
Set access boundaries, policy checks, approval points, escalation paths, and cost controls around the work that can create material risk.
03Operate, review, and improve
Observe runs, decisions, exceptions, spend, and outcomes. Use the evidence to rank gaps and decide which controls or agents deserve more scope next.
Shared evidence / one run, four views
What every team can prove from the same run.
Each stakeholder sees the part of the record they need without asking engineering to reconstruct the run from separate systems.
Risk
What is allowed to act?
Ownership, access scope, policy checks, exceptions, and review paths.
Engineering
What happened in the run?
Prompts, context, model and tool calls, retries, latency, and outcomes.
Compliance
What proves the decision?
Policy results, human rationale, approval records, and exportable evidence.
Finance
What did autonomy cost?
Spend by agent, model, workflow, run, and outcome — attached to the work.
FAQs
Questions worth answering before an agent reaches production.
Have a question specific to your stack?
Contact usIs Kommit another AI agent?
No. Kommit sits above the agents, models, tools, repositories, data sources, and cloud systems already doing the work. It is the control and evidence layer around them.
Do we have to replace our current models or tools?
No. The product is designed to govern a mixed stack. Teams can keep their provider and tooling choices while applying a consistent operating model across them.
Where does human approval happen?
A sensitive action can pause inside the run. The reviewer sees the relevant context, intended tool action, policy result, cost, and exception before approving or returning it.
What evidence stays with a run?
The record can include prompts, retrieved context, model and tool calls, policy checks, approvals, exceptions, spend, outputs, and outcomes, depending on the connected system and configured boundary.
Can we begin with an audit instead of a full rollout?
Yes. The 14-day audit pilot inventories the active agent surface, identifies governance and evidence gaps, and produces a prioritized plan for the next controls.
Are the interfaces on this page live customer data?
No. They are explicitly labeled product previews with illustrative data. Availability and integration depth depend on the selected deployment and connected systems, which we confirm during the access review.
Ready to account for every agent?
Start with your current stack. We will map the agent surface, review the evidence gaps, and show where policy or human review belongs next.