Applications based on LLMs are everywhere today. But in companies, many projects still struggle to go beyond the prototype stage. The reality of 2025 is that success in production no longer depends solely on the quality of the model, but on the ability to manage an ecosystem of intelligent agents, orchestrated in a reliable and scalable way. This approach, which can be described as agentic LLMOps, is becoming central to automating and optimizing complex business processes.

From Traditional LLMOps to Agentic Architecture

Historically, LLMOps focused on a few pillars: prompt management, fine-tuning, model deployment, and performance monitoring. The arrival of the agents has changed the situation. Today, it is no longer enough to deploy a model; it is necessary to manage a system where each agent has a profile, permissions, tools (APIs, databases, RAG, specific functions), knowledge, and a well-defined role.

Adding tools to an agent with Orchestrate

Collaboration between agents becomes a major issue: some agents schedule, others execute, and some validate the results or interact with the user. We are seeing patterns such as ReAct, Self-Refine, or hierarchical planning approaches emerge with several agents working in parallel. These systems use protocols such as MCP (Model Context Protocol) or ACF (Agent Connect Framework) to exchange information and events in a standardized way.

WatsonX: The Pillar of the Agentic Stack

IBM has designed WatsonX as a complete ecosystem to cover all the needs of this new generation of architectures.

• watsonx.data – the data engine

In 2025, watsonx.data goes far beyond a simple data lakehouse. It natively integrates vector databases, such as Milvus, making the implementation of Retrieval-Augmented Generation (RAG) more efficient, with accuracy gains of up to 40% on unstructured data. Integration with DataStax optimizes NoSQL workloads, and the use of Apache Gluten accelerates Spark processing, significantly reducing data pipeline latency.

• watsonx.ai – the studio for models and agent

WatsonX allows you to fine-tune, evaluate, and deploy models (open source or your own), whether open source or proprietary. But in 2025, it will also become an agent lab: the AgentLab and the Agent Development Kit (ADK), compatible with Python 3.11 to 3.13, will make it possible to create modular and collaborative agents, perfectly integrated into complex workflows.

• watsonx.governance – the trust layerai g

Governance is becoming essential. watsonx.Governance ensures compliance with the EU AI Act, monitors bias, provides model explanations, and facilitates continuous monitoring. This layer of control is crucial for putting accountable and audited agent systems into production.

• watsonx Orchestrate – the heart of agent orchestration

It is the real conductor. Orchestrate supports multi-agent scenarios, with flexible and adaptive workflows. Its No-Code Agent Builder allows business teams to deploy an agent in minutes, while developers can create more sophisticated agents through the ADK. The new Agent Catalog offers hundreds of pre-built agents for HR, sales, or logistics, allowing an immediate start on concrete use cases.

• AI Gateway – a unified access point

The AI Gateway connects OpenAI, Anthropic, NVIDIA, Cerebras, and other models from a single point. It offers intelligent routing, fallback mechanisms, cost management, and centralized telemetry, making it easy to govern and optimize performance.

Observability and continuous optimization

Observability is no longer just a dashboard of metrics. You must trace the reasoning of the agents, detect infinite loops, planning errors, or biases. Tools like Langfuse allow you to view and replay the entire sessions, while LangSmith offers in-depth analysis of prompts and workflows in a dev or production environment.

Organizations can set up user feedback loops, monitor success and escalation rates, and dynamically adjust agent scheduling or collaboration strategy.

Best practices for deployment in 2025

Deploying an agentic system in production requires going beyond traditional CI/CD. It is necessary to define key metrics (latency, cost per request, error rate, behavioral drift), set up real-time dashboards, provide automatic rollback mechanisms in the event of a problem, and set up A/B testing routines to compare different agent reasoning strategies.

Conclusion

With Watsonx and Orchestrate, IBM offers much more than a platform for deploying models: it is an infrastructure designed for the agentic LLMOps. It helps manage data, models, and compliance, while orchestrating intelligent agents that can act autonomously, collaborate, and improve over time.

This paradigm, centered on distributed intelligence and agent orchestration, prepares companies for a new generation of applications that are more agile, more transparent, and more useful daily.