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EnglishHow Does Dataiku Manage and Govern AI Agents Across a Business?
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Dataiku approaches agent management as a visibility and control layer: it lets you see every AI agent running across your business, wherever it was built, and track cost, performance, and risk in one place. That makes it possible to scale the agents that deliver value and catch the ones that don't. This applies to organizations that already have agents spread across multiple tools and teams and need a single place to govern them, rather than to someone still deciding whether to build a first agent.
What "agent management" means on the platform
According to Dataiku's own description, the capability is framed around one goal: "Know if your AI agents are delivering value." The platform positions this as Agent Management, sitting alongside its broader governance offering.
The core promise is deliberately cross-platform. Dataiku states you can "know what every agent is doing, wherever it was built." That phrasing matters — the value is not limited to agents created inside Dataiku. If your organization has agents originating from different teams or tools, the management layer is meant to surface them together rather than forcing you to check each system separately.
The three things it tracks
Dataiku describes tracking three dimensions in one place:
| Dimension | What it answers | Why it matters |
|---|---|---|
| Cost | What are agents spending? | Prevents silent budget drift as agent count grows |
| Performance | Are agents delivering value? | Separates agents worth scaling from ones to retire |
| Risk | What could go wrong? | Keeps governance attached to every running agent |
Because these are tracked together, the platform frames the outcome as a single decision loop: scale what works, catch what doesn't. Tracking cost without performance, or performance without risk, would leave gaps — the combined view is the point.
How governance fits in
Dataiku presents governance as the mechanism that makes agent scale sustainable. Its stated formula for "AI success" combines people, orchestration, and governance, built for enterprise scale. In the agent context, governance is what turns a scattered set of agents into something you can manage as a portfolio.
This connects to a broader pattern the platform describes: orchestrating models, agents, rules, and human review into governed workflows that run business processes end to end. Agent management is the oversight layer over that orchestration — you govern agents the same way you govern the workflows they participate in.
What this looks like in practice
Dataiku cites customer outcomes that illustrate the scale involved:
- Roche — described as transforming workflows with agentic AI.
- OHRA — described as redefining insurance and efficiency with AI.
- Aviva — described as powering insurance with data, machine learning, and AI.
These are presented as examples of agentic AI delivering results under the platform's governance model. The specific mechanics of each deployment aren't detailed in the source material, so treat them as directional evidence that the approach is used at enterprise scale rather than as a step-by-step blueprint.
When this approach fits — and when it doesn't
It fits if:
- You already have agents running across multiple teams or tools and lack a unified view.
- You need to justify agent spend and retire underperformers.
- Governance and risk tracking are requirements before you scale further.
It may not be the starting point if:
- You haven't built agents yet — the value here is managing what exists.
- Your agents live in a single system you already monitor well.
- You need pricing or deployment specifics; the source page does not state pricing, plan limits, or login requirements, so those must be confirmed directly with Dataiku.
How to evaluate it for your organization
- Inventory your agents. List every agent running across teams and note where each was built. If that list is hard to assemble, that's the gap this addresses.
- Check the three dimensions. For each agent, can you currently answer cost, performance, and risk questions? Any "no" is a candidate for centralized tracking.
- Define your scale/retire criteria. Decide in advance what performance threshold justifies expanding an agent and what triggers retirement.
- Confirm fit with Dataiku directly. Since the source material doesn't cover pricing, trial terms, or technical prerequisites, verify those against Dataiku's current documentation and sales process.
The underlying logic is straightforward: agents multiply faster than manual oversight can follow, so the management layer has to be as centralized as the agents are distributed. Dataiku's answer is to make that layer about visibility first — see everything, measure it on cost, performance, and risk, then act.