Most AI teams can build a demo agent in days. Turning that demo into something production-ready that meets enterprise expectations is where progress stalls.

Weeks of iteration become months of integration, and suddenly the project is stuck in PoC purgatory while the business waits.

Turning prototypes into production-ready agents isn’t just hard. It’s a maze of tools, frameworks, and security steps that slow teams down and increase risk.

In this post, you’ll learn step by step how to build, deploy, and govern them using the Agent Workforce Platform from DataRobot.

Why teams struggle to get agents into production 

Two factors keep most teams stuck in PoC purgatory:

1. Complex builds
Translating business requirements into a reliable agent workflow isn’t simple. It requires evaluating countless combinations of LLMs, smaller models, embedding strategies, and guardrails while balancing strict quality, latency, and cost objectives. The iteration alone can take weeks.

2. Operational drag
Even after the workflow works, deploying it in production is a marathon. Teams spend months managing infrastructure, applying security guardrails, setting up monitoring, and enforcing governance to reduce compliance and operational risks.

Today’s options don’t make this easier:

• Many tools may speed up parts of the build process but often lack integrated governance, observability, and control. They also lock users into their ecosystem, limit flexibility with model selection and GPU resources, and provide minimal support for evaluation, debugging, or ongoing monitoring.
  
• Bring-your-own stacks offer more flexibility but require heavy lifting to configure, secure, and connect multiple systems. Teams must handle infrastructure, authentication, and compliance on their own — turning what should be weeks into months.
  

The result? Most teams never make it past proof of concept to a production-ready agent.

A unified approach to the agent lifecycle

Instead of juggling multiple tools for build, evaluation, deployment, and governance, the Agent Workforce Platform brings these stages into one workflow while supporting deployments across cloud, on-premises, hybrid, and air-gapped environments.

• Build anywhere: Develop in Codespaces, VSCode, Cursor, or any notebook using OSS frameworks like LangChain, CrewAI, or LlamaIndex, then upload with a single command.
  
• Evaluate and compare workflows: Use built-in operational and behavioral metrics, LLM-as-a-judge, and human-in-the-loop reviews for side-by-side comparisons.
  
• Trace and debug issues quickly: Visualize execution at every step, then edit code in-platform and re-run evaluations to resolve errors faster.
  
• Deploy with one click or command: Move agents to production without manual infrastructure setup, whether on DataRobot or your own environment.
  
• Monitor with built-in and custom metrics: Track functional and operational metrics in the DataRobot dashboard or export your own preferred observability tool using OTel-compliant data.
  
• Govern from day one: Apply real-time guardrails and automated compliance reporting to enforce security, manage risk, and maintain audit readiness without extra tools.

Enterprise-grade capabilities include:

• Managed RAG workflows with your choice of vector databases like Pinecone and Elastic for retrieval-augmented generation.
• Elastic compute for hybrid environments, scaling to meet high-performance workloads without compromising compliance or security.
• Broad NVIDIA NIM integration for optimized inference across cloud, hybrid, and on-premises environments.
• “Batteries included” LLM access to OSS and proprietary models (Anthropic, OpenAI, Azure, Bedrock, and more) with a single set of credentials — eliminating API key management overhead.
• OAuth 2.0-compliant authentication and role-based access control (RBAC) for secure agent execution and data governance.

From prototype to production: step by step

Every team’s path to production looks different. The steps below represent common jobs to be done when managing the agent lifecycle — from building and debugging to deploying, monitoring, and governing.

Use the steps that fit your workflow or follow the full sequence for an end-to-end process.

1. Build your agent

Start with the frameworks you know. Use agent templates for LangGraph, CrewAI, and LlamaIndex from DataRobot’s public GitHub repo, and the CLI for quick setup.

Clone the repo locally, edit the agent.py file, and push your prototype with a single command to prepare it for production and deeper evaluation. The Agent Workforce Platform handles dependencies, Docker containers, and integrations for tracing and authentication.

2. Evaluate and compare workflows

After uploading your agent, configure evaluation metrics to measure performance across agents, sub-agents, and tools.

Choose from built-in options such as PII and toxicity checks, NeMo guardrails, LLM-as-a-judge, and agent-specific metrics like tool call accuracy and goal adherence.

Then, use the agent playground to prompt your agent and compare responses with evaluation scores. For deeper testing, generate synthetic data or add human-in-the-loop reviews.

3. Trace and debug

Use the agent playground to view execution traces directly in the UI. Drill into each task to see inputs, outputs, metadata, evaluation details, and context for every step in the pipeline.

Traces cover the top-level agent as well as sub-components, guard models, and evaluation metrics. Use this visibility to quickly identify which component is causing errors and pinpoint issues in your code. 

4. Edit and re-test your agent

If evaluation metrics or traces reveal issues, open a code space in the UI to update the agent logic. Save your changes and re-run the agent without leaving the platform. Updates are stored in the registry, ensuring a single source of truth as you iterate.

This is not only useful when you are first testing your agent, but also over time as new models, tools, and data need to be incorporated to upgrade it.

5. Deploy your agent

Deploy your agent to production with a single click or command. The platform manages hardware setup and configuration across cloud, on-premises, or hybrid environments and registers the deployment in the platform for centralized tracking.

6. Monitor and trace deployed agents

Track agent performance and behavior in real time with built-in monitoring and tracing. View key metrics such as cost, latency, task adherence, goal accuracy, and safety indicators like PII exposure, toxicity, and prompt injection risks.

OpenTelemetry (OTel)-compliant traces provide visibility into every step of execution, including tool inputs, outputs, and performance at both the component and workflow levels.

Set alerts to catch issues early and modularize components so you can upgrade tools, models, or vector databases independently while tracking their impact.

7. Apply governance by design

Manage security, compliance, and risk as part of the workflow, not as an add-on. The registry within the Agent Workforce Platform provides a centralized source of truth for all agents and models, with access control, lineage, and traceability.

Real-time guardrails monitor for PII leakage, jailbreak attempts, toxicity, hallucinations, policy violations, and operational anomalies. Automated compliance reporting supports multiple regulatory frameworks, reducing audit effort and manual work.

What makes the Agent Workforce Platform different

These are the capabilities that cut months of work down to days, without sacrificing security, flexibility, or oversight.

One platform, full lifecycle: Manage the entire agent lifecycle across on premises, multi-cloud, air-gapped, and hybrid environments without stitching together separate tools.

Evaluation, debugging, and observability built in: Perform comprehensive evaluation, trace execution, debug issues, and monitor real-time performance without leaving the platform. Get detailed metrics and alerting, even for mission-critical projects.

Integrated governance and compliance:  A central AI registry versions and tracks lineage for every asset, from agents and data to models and applications. Real-time guardrails and automated reporting eliminate manual compliance work and simplify audits.

Flexibility without trade-offs: Use any open source, proprietary framework, or model on a platform built for enterprise-grade security and scalability.

From prototype to production and beyond

Building enterprise-ready agents is just the first step. As your use cases grow, this guide gives you a foundation for moving faster while maintaining governance and control.

Ready to build? Start your free trial.

The post Are your AI agents still stuck in POC? Let’s fix that. appeared first on DataRobot.

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When planning cycles stall, business outcomes suffer. Static forecasts and slow collaboration keeps teams from responding to change, leaving businesses a step behind. 

SAP provides a powerful foundation for enterprise planning and operations. But as market conditions shift faster than ever, teams need new ways to respond with speed, precision, and adaptability.

Agentic AI introduces intelligent, self-adaptive automation into the picture, enhancing existing planning processes so teams can move faster and make better decisions.

Now, with the SAP® Endorsed App from DataRobot, organizations can bring that intelligence directly into their SAP environment, extending the value of their investment and enabling more responsive, future-ready planning. 

What’s new, and why it matters

AI Apps and Platform by DataRobot are now SAP® Endorsed and available on the SAP Store.

This designation is more than a badge. It’s a mark of technical excellence and proven customer value. Earning it means DataRobot has met SAP’s premium certification standards, including security reviews, cloud integration tests, and static code analysis.

For customers, this opens a clear, low-risk path to adopting agentic AI with confidence, including:

• Avoiding workarounds or patchwork integrations
• Keeping your existing systems. No rip-and-replace required
• Running AI securely and natively inside your SAP environment, from day one
  

A smarter path to enterprise planning

Many SAP customers face similar planning roadblocks:

• Fragmented systems and rigid workflows slow down decisions and force manual workarounds
• Disconnected roles and teams struggle to align or respond quickly when conditions change
• Slow, complex data integration makes it hard to adapt plans in real time
• AI investments get stuck in pilots, never delivering value at scale
  

SAP customers are already managing complex data and planning processes across finance, operations, and supply chain. But as needs evolve, many teams are looking for new ways to respond faster to change, extract insight from growing data sets, and operationalize AI across business functions.

To close these gaps, SAP customers are turning to agentic AI apps that can adapt, automate, and scale alongside their existing workflows.

Agentic AI modernizes enterprise planning by:

• Empowering business users with intuitive agentic AI interfaces
• Delivering more accurate, self-adapting forecasts across use cases, from demand and headcount to resource allocation and beyond
• Reducing dependency on manual updates and isolated workflows, accelerating planning across functions

The agentic AI Apps and Platform that DataRobot provides are designed to complement SAP, layering intelligent decision support, automation, and learning into the tools your teams already rely on.

Agentic AI planning for finance teams

Finance leaders face increasing pressure to forecast, advise, and act faster than ever, with tighter margins and greater precision. Agentic AI helps them meet the moment.

With the SAP Endorsed App from DataRobot, finance teams can:

• Predict cash flow gaps early to free up working capital
• Catch financial anomalies before they impact the bottom line
• Automate invoice validation to reduce errors and accelerate approvals
• Confidently model revenue scenarios to guide strategic decisions
• Assess credit risk in real time to avoid delays and disruptions

This shifts finance from reactive to strategic, enabling faster insight, earlier action, and better decision-making that helps the business stay ahead.

Supply chain teams: prevent delays, reduce waste, and move faster

Modern supply chains can’t afford blind spots. From demand planning to delivery, even minor delays can ripple across the business.

SAP already plays a central role in planning and supply chain execution. With agentic AI, teams can go further, adapting in real time, anticipating disruptions, streamlining decisions, and simplifying operations.

With this combination, supply chain teams can:

• Forecast demand more accurately to reduce stockouts and excess inventory
• Use real-time signals to manage delays and minimize disruptions
• Optimize production schedules to maximize output and resource use
• Plan labor more efficiently to align staffing with operational needs
• Predict maintenance needs early to prevent costly downtime

With agentic AI layered into SAP, supply chain teams adapt faster without adding new systems or complexity.

Explore what’s possible: Agentic AI that integrates, accelerates, and delivers

This isn’t a bolt-on demo or shadow tool. It’s an enterprise-grade AI platform designed to work inside your SAP environment.

It connects directly to SAP data and workflows, delivering: 

• Agentic AI decisions embedded in your SAP data layer
• Prebuilt apps and customizable templates for real-world use cases
• End-to-end governance and control built for enterprise standards
• Expert support to help you build, deploy, and scale agentic AI safely

The result: faster outcomes, more resilient planning, and a practical way to run your business.

Our SAP Endorsed App goes beyond technical alignment. They represent a new planning experience: one that’s intelligent, inclusive, and adaptive by design. It’s a shared vision for enabling intelligent enterprises, where planning isn’t just faster, but smarter, more adaptive, and more connected to real outcomes.

Explore our agentic AI apps on the SAP Store

The post Plans change. The SAP® Endorsed App from DataRobot keeps up. appeared first on DataRobot.

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