What is AgentOps?

AgentOps (agent operations) is a framework of tools for monitoring the “brain” of an AI as it makes decisions in real time. Think of it as a way to manage and set parameters for your autonomous AI “employee.” It helps make sure that when an agent is given a task, it completes it efficiently, safely, and without exceeding a set budget. 

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The actions of agents are nondeterministic—that is, decided by a series of random probability distributions. That means their actions can’t be precisely predicted. This lack of predictability helps agents find creative paths to solve problems. But in production, autonomy without explainability can become a liability. AgentOps helps mitigate that risk. 

Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention. With an emphasis on goal-oriented behavior, agentic AI can accomplish tasks by creating a list of steps and performing them autonomously.

Agentic AI is a way to combine automation with the creative abilities of a large language model (LLM). To put agentic AI into practice, you give an LLM access to external tools and algorithms that supply instructions for how the AI agents should use those tools.

AI agent vs. agentic AI

What’s the difference between an AI agent and agentic AI? An AI agent is a noun (“I’m building 3 agents.”) and agentic AI is descriptive (“We need to make our software more agentic.”).

An AI agent is a software entity built to work and perform a role within an agentic system. Agentic AI describes a system that can plan, make decisions, and take action toward goals with limited human guidance. Agentic AI refers to the behavioral characteristics of a system.

AgentOps serves both AI agents and agentic AI in different ways. 

For AI agents, AgentOps helps with:

• Identity and versioning: Tracks the differences in personas and abilities of agents.
• Tool management: Monitors which agents have access to which application programming interfaces (APIs) and databases.
• Cost and resource tracking: Tracks how much money agent A spends vs. agent B.

For agentic AI, AgentOps helps with:

• Traceability: Maps out the “thought tree,” or reasoning, so a human can see why the AI decided to do what it did (for example, why the AI performed step 3 before step 2).
• Success rates: Measures success of the overall agentic system you created.
• Hallucination detection: Catches errors in real time before the agent spends too many resources doing the wrong things.

AI agents and agentic workflows can be as autonomous as we program them to be. No matter where a workflow sits on the agentic spectrum, AgentOps is important for reliability and oversight. 

Agentic workflows can help creatively solve problems, but that creativity needs to be managed so systems don’t go rogue. AgentOps helps mitigate the risks of agentic AI by observing, evaluating, governing, and optimizing agentic systems.

Observability

Agents create a sense of “reason” in a think-act-observe loop. If an error occurs in this process, the whole task can be derailed. If an agent does something unexpected, you need to interrogate its logic to find the error. AgentOps provides a traceable line of reasoning so a human can see the root cause of a bad decision. 

Real-time evaluation

While your main agent is working, a secondary agent can be set up (via AgentOps processes) to supervise it. If the supervisory agent notices that the main agent is hallucinating or drifting away from its goal, it can pause the system or flag it for human intervention. 

Governance

When we delegate tasks to agents, we need to set guardrails. Guardrails are barriers that keep AI systems operating within defined boundaries. AgentOps lets you implement human-in-the-loop (HITL) checkpoints and make sure agents can’t perform high-stakes actions (like deleting files or spending money) without a human signing off on it first. 

Cost optimization

AgentOps provides the receipts to show you if the agent is being inefficient. For example, it might reach for a model that’s too expensive or solve a problem in a way that’s too complex and uses up too many resources. 

With AgentOps, you can set up your system with instructions like:

• “Stop the task if it costs more than US$5.00.”
• “Stop the task if it takes more than 20 steps to complete.”
• “Block the ‘delete’command.”

AgentOps is a critical element for those looking to implement sovereign AI practices. Sovereign AI is about owning technology, keeping data local, and making sure your AI systems reflect your values and legal requirements.

AgentOps provides transparency into our systems, which is important from a legal standpoint. After all, “the AI decided to do it” defense won’t hold up in court. 

We’re moving from using AI as a tool to answer questions to using it as a system that understands context. Therefore, organizations need to create semantic layers and Model Context Protocol (MCP) gateways that let an AI agent safely navigate an entire collection of enterprise data. AgentOps can help by:

• Tracking hardware resource use.
• Monitoring hallucination rates.
• Ensuring data stays encrypted.
• Providing an auditable log of actions made by the agent.
• Terminating a process should policy violations occur.

In a sovereign AI system, AgentOps can provide a verifiable record of decisions, data flow, and tool interaction so you can better understand how your system works. 

A fully agentic agent makes its own decisions, selects its own tools, and corrects its own errors. This involves a lot of complex decision making, which becomes a “black box” problem. 

A black box refers to an AI model that’s too complex to understand, doesn’t show its work, or both. It creates a scenario where no one—including the data scientists and engineers who created the algorithm—can explain exactly how the model arrived at a specific output. To solve the black box problem, we need explainable AI. 

Explainable AI is a philosophy and set of practices that aim to make the actions of AI understandable to humans. AgentOps is the toolkit that facilitates this. 

AgentOps can provide a chronological map of every reasoning loop, tool call, and observation made by an AI agent. This helps us understand why an agent chose to use 1 tool over another. It can also give humans a way to provide feedback via reinforcement learning to correct the agent if it makes a mistake. 

For example, AgentOps can supply an interface that lets humans read the reason the agent performed a task. Then we can tell the agent, “Step 3 was a bad decision; it used a model that was too expensive.” 

AgentOps is another addition to the “Ops” (operations) family (like DevOps, AIOps, MLOps, and LLMOps). Let’s take a moment to define the different types of ops and how they work together.

• DevOps is the foundation all other ops grew from. DevOps is a set of practices that aims to ensure any software can be built, tested, and deployed reliably. The goal of DevOps is to increase software delivery speed.
• AIOps (AI for IT operations) is about applying AI to DevOps. The goal of AIOps is to use AI to automate IT operations and prevent bugs before they happen. It helps monitor servers and prevent a crash.
• MLOps (machine learning operations) is about managing the lifecycle of a machine learning model. The goal of MLOps is to make sure the model’s accuracy doesn’t “drift” as new data comes in.
• LLMOps (large language model operations) is a subset of MLOps specifically for managing LLMs. The goal of LLMOps is to manage prompts, reduce hallucinations, and lower the cost of API calls. 

Read about AIOps with Red Hat 

What does all this have to do with AgentOps? 

To run a reliable business product with AgentOps, you must already have LLMOps and DevOps in place. AIOps and MLOps can be helpful, too. Let’s look at how they might all work together:

• DevOps: To create an agent, you need code. That code needs to be processed and transmitted through servers in a reliable and scalable way. DevOps makes sure this happens.
• LLMOps: LLMOps handles the logic of the user’s prompt and helps the agent translate it into a plan of action.
• MLOps: MLOps makes sure the machine learning models accessed by the agent are accurate. This may mean automatically updating the model with current data and ensuring the agent calls the newly updated model rather than an old version.
• AIOps: If a server crashes, it could trigger 1,000 alerts. AIOps can note that all those alerts are from the same event and prompt the human with just 1 “major incident” alert. This is more efficient and reduces confusion.

You should apply AgentOps to all phases of an agentic workflow, from operational foundation to safety measures and advanced scaling.

You want to build from an operational foundation. This means making sure the following systems are in place:

Standardized protocols

For agents to interact within a digital ecosystem, they need to share a common language with the tools they use. MCP enables a 2-way connection and standardized form of communication between AI applications and external services. Without a standardized protocol like MCP, agentic AI can think and plan but can’t interact with outside systems. 

Error-handling mechanisms

When working with agentic workflows, it’s important to account for instability and incapability. This means creating insurance policies within your system that can handle errors when they arise—like having an airbag ready in case of a car crash. These are sometimes called “self-healing” capabilities. 

• Retry logic: Occasionally, elements within the system your agent uses will temporarily fail, causing instability. Rather than shutting down the whole workflow, building retry logic is a good line of defense. This means creating instructions for how to proceed and self-correct to avoid infinite reasoning loops (and costly bills).
• Fallback model: This secondary model can take over if the primary model becomes incapable or too expensive. For example, if your agent is using OpenAI and it goes down, your agent can switch to a local model, like Llama 3. 

Tool guardrails

If the error-handling mechanisms are the airbags that deploy in reaction to a crash, guardrails are the brakes that aim to prevent a crash in the 1st place. You can set rules for your agent to follow, such as deleting files only if a human approves it.

Governance and compliance

Through governance and compliance, you make sure all your agent’s actions are logged and accounted for. This is especially important in fields that require strict adherence to privacy laws like General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA).

Memory optimization 

Agents can get “confused” if their conversation history is too long. It overwhelms their context window and can cause attention drift, leading to hallucinations or a breakdown in their ability to complete a goal. You can optimize memory with vLLM, which uses PagedAttention (as a memory management technique) to help agentic systems handle long-context histories efficiently and at scale. vLLM is especially useful for agentic workflows, because it supports high performance even as complexity increases.

Learn more about vLLM 

Multiagent collaboration frameworks

Multiagent collaboration is the practice of assigning distinct roles, memories, and tools to multiple, independent LLMs. You might have 1 agent acting as a “researcher” and another as a “builder” passing messages back and forth to create a final output. The goal of multiagent collaboration is to overcome the limitations of a single model by forcing agents to work together and critique each other. 

Autonomy dilemma

Independence can lead to amazing outcomes—or chaos. Finding the right amount of agent autonomy is tricky and requires lots of time working with guardrails to create the right balance. To manage this, developers should implement human-in-the-loop checkpoints to make sure the agent acts only within approved boundaries.
 

Ethical and compliance issues

Agents are goal oriented and might “creatively” decide to take shortcuts, like offering an unauthorized discount to a customer to close a deal. This can violate fair-lending laws or internal policies. Solving this requires policy-enforcement layers and auditing to ensure agentic actions comply with legal and corporate standards. 
 

Privacy concerns

Because agents can access many data sources, there’s a risk they can inadvertently share sensitive or private information with someone who shouldn’t have access. You can protect against this with a list of forbidden actions. 
 

Unexpected costs

Agents work in a loop (think-act-observe), which can quickly (and expensively) spiral. It’s important to think ahead and implement budget caps and safety nets to avoid using up too many resources. 
 

Scalability

Running 1 agent on 1 laptop is very different from running 1,000 agents that are performing 1,000 workflows simultaneously. Using tools like distributed inference, llm‑d, and vLLM helps manage the massive number of memory and compute requirements needed to run a fleet of agents. 

Here are a few examples of how an enterprise might use AgentOps to help manage their workflows:

The financial watchdog

A team of agents monitors thousands of daily transactions and flags fraud or policy violations. They work by ingesting data, cross-referencing it with internal policies, and flagging suspicious activity for human review. 

The autonomous help-desk helper

Agents are given the ability to test and fix code in a sandbox environment. When a work ticket is submitted, the agent reproduces the bug in a sandbox, writes a potential fix, then runs tests. When it has a good idea of how to fix the problem, it notifies a human to review and approve the agent’s work.

The supply-chain supervisor

An agentic system monitors global weather, shipping strikes, and port congestion. It alerts the team to weather disturbances, calculates the cost of rerouting, and proposes a change. 

Red Hat® AI operationalizes the full lifecycle of an agent through a dedicated AgentOps control plane. This ensures every deployment is safeguarded, observable, and efficient across your hybrid cloud environment. 

The platform provides enterprise-grade governance through integrated safety guardrails. Its underlying infrastructure uses vLLM and llm‑d for high-performance distributed inference, so you can scale resource-intensive workflows—from on-premise to edge environments.

Red Hat AI offers fast, flexible, and efficient inference through its vLLM-powered server. It reliably connects models to your data to unify the customization and development of specialized agents on a single platform. Built on an open source foundation, our AI products give you full control of AI workflows from end to end at any scale.