By Cesar Miguelañez — 19 Jan 2026

How to Deploy Agentic AI in Production Safely

Discover key strategies for deploying agentic AI in production, including lessons learned, best practices, and real-world examples from industry leaders.

Artificial Intelligence has revolutionized countless industries, but deploying it in production environments brings unique challenges. Natasha, a staff AI engineer at Databricks, shares her insights and lessons from real-world deployments of Agentic AI systems. Her experience, working with large-scale enterprises, highlights the importance of balancing technical considerations with business constraints to ensure reliable, scalable, and safe AI-powered products.

This article synthesizes Natasha’s valuable takeaways, blending her technical expertise with practical recommendations for both technical practitioners and product managers. Whether you’re developing or managing AI systems, this guide will help you understand the nuances of deploying Agentic AI solutions effectively.

What Is Agentic AI in Production?

Agentic AI refers to systems where AI models, such as large language models (LLMs), are structured to perform tasks through defined steps, often mimicking decision-making processes. While the term might evoke images of autonomous agents operating with full freedom, Natasha clarifies that most real-world use cases rely on deterministic, modular systems rather than fully autonomous agents.

At its core, deploying Agentic AI successfully is less about the AI itself and more about adhering to time-tested software engineering principles. This involves integrating the AI into a robust system architecture, ensuring quality and safety, and focusing on measurable outcomes.

Key Challenges in Deploying Agentic AI

Natasha highlights several common pitfalls that can derail AI projects during their transition from proof of concept (PoC) to production:

1. Ignoring Constraints

Many teams focus on exciting use cases without considering constraints like budget, security, or infrastructure requirements.
Example: One client expected 20,000 daily users but lacked the infrastructure budget to support such traffic. Understanding these constraints early can prevent wasted effort.

2. Overtrusting LLMs

Relying too heavily on LLMs without carefully engineering the system around them can lead to unpredictable outputs.
Tip: Break the process into clear, modular components. For example, a retrieval-augmented generation (RAG) pipeline ensures structured steps, such as retrieving relevant information before allowing the LLM to generate a response.

3. Skipping DevOps Best Practices

Many projects fail due to inadequate planning around deployment, monitoring, and governance.
Recommendation: Start with a minimum viable product (MVP) and incorporate DevOps processes early, such as version control and CI/CD pipelines, to ensure a smooth transition to production.

4. Unpredictable User Inputs and Outputs

Users may interact with AI systems unpredictably, asking questions outside the intended scope or generating malformed outputs.
Solution: Implement safety guardrails and robust evaluation mechanisms to handle edge cases effectively.

5. Undefined Quality Metrics

Without clear benchmarks, teams struggle to measure the success of their AI systems.
Natasha emphasizes defining quality metrics based on the system’s purpose, whether it’s text generation, classification, or semantic search.

6. System Complexity

AI systems often involve multiple moving parts, increasing the likelihood of errors.
Debugging becomes challenging without tools like observability and tracing frameworks.

A Case Study: Flow Health’s AI-Powered Solution

Flow Health

To illustrate successful deployment, Natasha shares the example of Flow Health, a women’s health platform. With over 80 million monthly active users, Flow Health integrated Agentic AI to provide personalized health recommendations through chat-based interactions.

Key Factors Behind Their Success:

Unified Data Platform: By consolidating fragmented data into one system using Databricks, Flow Health improved scalability and accuracy.
Fine-Tuned Models: They fine-tuned open-source LLMs like LLaMA, leveraging techniques such as synthetic data generation for high-quality outputs.
Cost Efficiency: By adopting smaller, optimized models, they balanced performance with cost savings.
Safety and Privacy: Their solution adhered to strict medical accuracy and privacy standards, ensuring trustworthiness.

This case study highlights how precision, automation, and thoughtful system design can enable impactful AI deployments.

Best Practices for Agentic AI Deployment

1. Design for Determinism

Avoid fully autonomous agents when possible. Most use cases benefit from pre-defined, deterministic steps for better reliability and control.

2. Modular System Architecture

Separate components for flexibility and maintainability. For example, isolate the retrieval, embedding, and generation steps in a RAG pipeline.

3. Prioritize Observability

Use tracing frameworks to monitor inputs, outputs, and latency across components. Natasha recommends tools like MLflow for this purpose.

4. Focus on DevOps Early

Build staging environments, automate deployments, and ensure infrastructure capacity aligns with expected usage patterns.

5. Continuous Evaluation

Incorporate LLM judges or user feedback loops to monitor the system’s quality over time. Auto-generated training data from user interactions can refine the model further.

6. Plan for Cost Management

Track infrastructure costs closely, especially for high-traffic applications using pay-per-token services.

7. Governance and Security

Define user access roles and adhere to regulatory requirements. For example, European clients often require data to remain within EU cloud regions.

Tools and Frameworks to Consider

Natasha highlights several tools that streamline Agentic AI development and deployment:

MLflow: Open-source platform offering LLM evaluation, observability, and tracing.
Managed Model APIs: Services like Databricks Foundation Model API or OpenAI endpoints simplify scalability.
Vector Stores: Essential for retrieval-based systems, these tools enable efficient data storage and querying.
Custom LLM Judges: Tailor evaluations to specific use cases, such as enforcing structured outputs or safety constraints.

Key Takeaways

Understand Constraints: Before diving into a project, assess technical and business limitations, including budget, infrastructure, and compliance.
Build Modular Systems: Design applications with clear, deterministic steps to reduce complexity and improve reliability.
Start Small: Focus on an MVP and iterate based on user feedback and quality metrics.
Emphasize Observability: Use tools like MLflow to monitor system performance, debug errors, and optimize costs.
Plan for Cost Control: Track infrastructure expenses to avoid unexpected cloud bills.
Define Quality Metrics: Establish benchmarks tailored to your application’s goals, such as accuracy, safety, or structured output adherence.
Leverage Tracing: Debug latency and monitor interactions across system components to improve performance.
Prioritize Governance: Ensure proper access controls and compliance with data regulations.

Final Thoughts

Deploying Agentic AI in production is not about chasing buzzwords or building overly complex systems - it’s about applying solid engineering principles to deliver reliable, scalable, and high-quality solutions. As Natasha emphasizes, production AI is as much about the "boring" steps of monitoring, versioning, and governance as it is about the AI itself.

By adhering to the lessons shared here, teams can navigate the challenges of transitioning from PoC to production, creating impactful AI systems that generate real business value. For technical practitioners and product managers alike, the path to successful deployment lies in collaboration, meticulous planning, and a relentless focus on quality.

Source: "Agentic AI in Production: Lessons from Real-World Deployments | Natasha Savic | DSC EUROPE 25" - Data Science Conference, YouTube, Dec 31, 2025 - https://www.youtube.com/watch?v=mMQq-KDKEbA