Dataiku Agent Hub: your enterprise agent control plane
Design, orchestrate, deploy, and govern AI agents in one centralized system. Agent Hub in Dataiku brings agents, tools, models, and enterprise data together with the visibility, control, and lifecycle management required to scale in production.
Centralized agent creation and orchestration
Design agents using visual workflows or full code, connect them to enterprise data and tools, and orchestrate multi-step agentic processes in one governed environment.
Enterprise-grade oversight and visibility
Track agent behavior, usage, outcomes, and dependencies in a single workspace. Maintain accountability, auditability, and performance insight across every deployed agent.
Governed reuse at scale
Standardize prompts, tools, guardrails, and templates so teams innovate within clear boundaries, accelerating delivery without introducing risk or fragmentation.
Design and operationalize agents without fragmentation
Agent Hub brings everything needed to design, deploy, and scale AI agents into one governed system. Build visually or in code, connect to enterprise data and tools, and orchestrate multi-step agent workflows all in one place. Agents aren’t side experiments. They’re production assets, built on approved data, reusable components, and shared standards so teams can scale impact without reinventing the wheel.
Full lifecycle control for every agent
Agent Hub centralizes testing, deployment, monitoring, and oversight. See how agents behave, what tools they call, and what outcomes they generate with auditability and accountability by default. Governance isn’t bolted on. It’s embedded into how agents are built and run, so enterprises move fast without losing visibility, control, or architectural consistency.
Explore more agentic ai features
The Dataiku LLM Mesh
Connect to any LLM provider or self-hosted model, with centralized visibility and control across every connection.
LLM Guard Services
Standardize agent performance and alignment checks across use cases to maintain reliability and trust.
GenAIOps
Build, deploy, and monitor generative AI applications with full visibility and control.
Don't take our word for it
“The platform is intuitive, collaborative, and streamlines workflows from data prep to model deployment. Dataiku has truly transformed how we handle data!”
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Frequently asked questions
Dataiku provides an enterprise AI agent platform designed for secure, governed, and scalable deployment. Organizations can build, deploy, and orchestrate multi-agent workflows with centralized governance, approved LLM integrations through the LLM Mesh, and full lifecycle monitoring — ensuring agents operate safely in regulated environments.
Yes. Dataiku supports both no-code AI agent builders for business teams and full-code development for technical users. Teams can customize agent logic, orchestration, tools, integrations, and data access to fit unique enterprise workflows — whether building conversational AI, RAG applications, or autonomous task execution systems.
Dataiku includes built-in governance across the full agent lifecycle through centralized LLM routing and model management (LLM Mesh), role-based access controls and approvals, guardrails for prompts, tools, and data access, audit trails and monitoring dashboards, AI safety and compliance frameworks. This makes it a strong choice for enterprises seeking governed AI agent orchestration with full compliance controls.
Agents connect to approved enterprise data sources through governed access policies. The LLM Mesh enforces controlled use of data, tools, and models, ensuring privacy, relevance, and regulatory compliance while enabling context-aware AI workflows.
Dataiku provides centralized LLM usage management, including: model routing optimization, quotas and budget controls, usage tracking across agents and teams, spend visibility dashboards. This allows enterprises to proactively manage GenAI costs while scaling multi-agent deployments.
Built-in testing, evaluation, and monitoring capabilities allow continuous validation of agent outputs. Organizations can run automated evaluations, monitor drift and performance degradation, enforce quality assurance workflows, and mprove reliability as business needs evolve.