What is AgentOps? What it does and how it powers AI agents

Why AgentOps matters for enterprises

Well-designed artificial intelligence (AI) systems and agentic AI workflows bring deep learning, comprehensive analyses and cost-effective workflow automation and enhancement to organizations of all types and sizes.

But as AI adoption accelerates and AI agents become more numerous and autonomous, organizations must incorporate management and oversight into their AI strategies and AI agent lifecycles. AgentOps provides this oversight in five major areas:

• Reliability and performance. AgentOps oversees the decisions and interactions of AI agents, systems, data and users and analyzes those behaviors to ensure the AI system provides accurate outcomes and performs within acceptable boundaries. AgentOps aids end-to-end observability, helping ensure AI system explainability and transparency. Any anomalies, errors or faults are debugged and resolved faster.
• Integrations and interactions. AgentOps integrates AI agents and AI systems with key resources, including databases, customer relationship management and enterprise resource planning systems. Large collections of AI agents also mean extraordinarily complex workflows. AgentOps supports agentic AI workflows, enabling organizations to handle these complexities more effectively.
• Security and compliance. AgentOps employs security controls to prevent common AI agent threats, including prompt injection attacks, inappropriate interactions or inadvertent data leaks. AgentOps supports regulatory compliance through detailed logs, ensuring all AI agent reasoning, decisions, and actions are reviewable and explainable. This is particularly important for healthcare, financial and scientific organizations.
• Learning and optimization. AI agents learn and adapt to changing data and business needs. AgentOps helps organize and oversee these dynamic iterations, measuring the changes to AI agent or workflow effectiveness with current business objectives. Also, by collecting and analyzing logs and feedback of AI agent behavior, AgentOps drives optimal training and tuning outcomes.
• Resource use and cost effectiveness. AI systems consume considerable resources. AgentOps monitors and reports resource consumption and predicts associated costs—especially important when AI systems deploy to the public cloud. This also helps organizations optimize resource use and maintain an acceptable cost-to-performance ratio.

How does AgentOps work?

AI agents, increasingly complex entities designed for dynamic and unpredictable situations, pose serious challenges for today's adopters. It's difficult to oversee their decision-making and track their accuracy, potentially yielding suboptimal outcomes for users, compromising security and violating compliance obligations—all blows to the business.

AgentOps fills this management gap, providing a framework of related tools designed to manage AI agents throughout their lifecycle, which commonly includes:

• Define objectives for the AI agent.
• Design the AI agent.
• Build and test the AI agent.
• Deploy and monitor the AI agent.
• Analyze collected data to refine and update the AI agent.

Given this extensive scope, AgentOps platforms necessarily provide a wide array of features and capabilities to address the following lifecycle phases:

Performance evaluation

AgentOps scrutinizes an AI agent's performance for accuracy, safety, coherence, fluency and context. Comprehensive debugging capabilities review execution or decision-making paths and identify recursive loops or other wasted processing activities. Collectively, these evaluations help developers understand an AI agent's decisions and actions.

Observability and explainability

Once built and ready for testing, AgentOps tracks many aspects of AI agent performance, including LLM interactions, agent latency, agent errors, interactions with external tools or services such as databases or other AI agents, as well as costs such as LLM tokens and cloud computing resources.

Performance parameters are often displayed as a dashboard, and detailed logs are reviewable, replaying agent behaviors to question and clarify agent execution: How were these decisions made and what resources or services were used that led to the agent's decision? That insight helps developers recognize algorithm problems or coding issues for correction and refinement.

Compliance and security

AI agents have extraordinary access to business data – stored, collected in real time or accessed through external sources. Much of this data is sensitive. Some contains personally identifiable information (PII), while other data has derogatory or profane content potentially harmful to the organization's reputation.

AgentOps' extensive logs are analyzed to reveal unintended or inappropriate sensitive content, from the accidental release of PII to the use of profanity in a prompt. AgentOps also reviews prompts for threats, including prompt injection attacks and improper user requests. These safeguards protect sensitive business data and maintain security, compliance and a bias-free environment.

Lifecycle management

AgentOps provides tools that support the entire AI agent lifecycle. They include design tools, building and testing features, deployment assistance to production environments and agent monitoring. Moreover, AgentOps drives ongoing optimization through adaptive learning and performance analyses.

Integrations

AgentOps platforms typically provide an assortment of integrations specifically intended to support AI agent development. Seek support with various open source and proprietary LLMs, as well as seamless integrations with existing AI agent frameworks, including:

• Agno.
• ChatDev.
• CrewAI.
• LangChain.
• LangGraph.
• Microsoft AutoGen.
• Microsoft Semantic Kernel.

Learn about the differences between open source platforms LangChain and Semantic Kernel.

Cost and resource use

It's rare for AI agents and AI systems to be designed, built and operated entirely in-house. Most AI systems mix agents, LLMs and data sources; some of these bring costs in licensing, per-call or per-token fees. Also, the computing resources, services and applications that support AI agents and AI systems, such as firewalls and databases, have a cost whether the resources come from a local data center or a cloud. AgentOps identifies and tracks associated AI agent costs, enabling organizations to understand and contain them.

Use cases of AgentOps

With its strong emphasis on AI agent observability and management, AgentOps is useful for many purposes across an agentic AI system. Common application areas include:

Agent software development

AI systems are rarely one size fits all. Instead, AI systems – and the AI agents that compose them – are built, tested, deployed and managed using traditional software development paradigms such as DevOps. This makes AgentOps tools ideal for testing and debugging work.

For example, AgentOps platforms replay execution step by step, letting developers review the agent's decision-making process and troubleshoot issues throughout its development. Similarly, AgentOps identifies poor coding techniques such as recursive or infinite loops, plus other inefficiencies that impair an agent.

AgentOps also helps developers perform blue/green testing among agent versions, comparing their performance, accuracy and computing cost before releasing the chosen agent to full production. Strong version control and rollback features aid developers with anomalies in testing and deployment, enabling fast response if the need arises.

Agent explainability

AI systems demand explainability throughout the lifecycle of every AI agent – initial development and testing, ongoing performance monitoring, plus compliance and security. Observing and understanding each AI agent's behavior helps identify errors or unexpected results in an agent's decision-making. It also locates performance impairments such as resource bottlenecks.

Compliance and security

AgentOps supports AI agent compliance and security. For example, it reviews detailed logs to analyze agent decision-making and ensure conformity with government and industrial regulations regarding accuracy, bias and ethical use. This process also underpins agent explainability.

AgentOps also provides strong security, identifying possible AI agent vulnerabilities and ensuring secure, reliable agent performance. For example, AgentOps protects against prompt injection attacks and exfiltration.

AI agent orchestration

An AI agent is rarely used alone. Instead, agents typically collaborate – each performing a specialized task – toward a common business goal. AI agent orchestration is necessary, and AgentOps is adept at observing interactions and data exchanges within complex, orchestrated AI systems. This pinpoints performance bottlenecks and resource inefficiencies that impair the greater AI system. AgentOps also oversees agentic AI workflows, improving their productivity.

Governance and adaptation

AgentOps is a centerpiece of AI governance. By analyzing and auditing detailed activity logs, it ensures AI systems and their agents follow business policies and support compliance and security postures.

AgentOps also tunes and refines AI agents over time, collecting and processing system outcomes and weighing user feedback on AI accuracy and results. This automates tuning and retraining for greater accuracy, and it’s vital for industry-specific, or vertical, AI systems.

Agent cost management

The hardware resources, data sources and software services typically needed for AI system operations are costly regardless of deployment site, local data center or public cloud. AgentOps helps with cost tracking and management.

For example, AgentOps monitors cloud resources allocated to the AI system, supporting proper resource scaling and cost containment. AgentOps also tracks the use, restrictions and costs associated with foundation models such as LLMs and other licensed AI components.

Comparing AgentOps to related frameworks

The modern IT lexicon includes numerous approaches that combine important practices with operations, or Ops. Table 1 below provides a simple summation of these approaches; most support or complement AgentOps in some form. Commonly related frameworks include:

• DevOps. This approach combines continuous software development – and delivery practices with operations deployment. This streamlines the software development process and empowers developers to deploy, validate and manage software releases with little, if any, direct involvement from IT. Developers who create and test AI agent code routinely use DevOps, driving new and updated AI agents to production quickly and efficiently.
• MLOps. This approach focuses specifically on the development, testing, deployment  and maintenance of machine learning (ML) software models, then extends development to operations. In effect, MLOps is DevOps for machine learning models. It pays particular attention to the reliability, scalability, explainability and maintainability of ML models from development to production. As with DevOps, MLOps relies heavily on automation and orchestration of the software development workflow. It includes ML-specific tasks such as data preparation, model training and ongoing model oversight. MLOps is key to AI developers working on ML models as foundations for AI agents and AI systems.
• LLMOps. This approach is tailored specifically to the development, testing, deployment and management of large language models, extending the development approach to production-level operations deployment. This includes complex model development of the LLMs themselves, large-scale training data preparation, prompt engineering for LLMs, LLM model training and tuning, LLM deployment and its ongoing monitoring, as well as post-deployment iteration, tuning and improvement.
• AIOps. Unlike other approaches listed here, AIOps specifically refers to the use of AI and machine learning technologies to improve, automate and orchestrate IT Ops. In practice, varied development paradigms  – DevOps through LLMOps, for instance – are employed to create AI agents and systems. However, the purpose of development efforts in AIOps situations is for IT-related tasks directly. AIOps relies on extensive data collected and analyzed across the IT infrastructure to assist IT staff in managing and optimizing highly sophisticated IT environments. This often includes broad use of automation and orchestration tools to streamline IT workflows. Moreover, it typically provides strong vertical AI system capabilities, including a detailed knowledge base and chatbot support using foundation models such as LLMs.

Table 1: Other approaches related to AgentOps

What's next for AgentOps?

AgentOps' ability to create, deploy, scale and manage AI agents is becoming as important to AI as automation and orchestration, bringing greater explainability, analytical understanding, autonomy and trust to AI agents. Three anticipated improvements to AgentOps include:

• Greater self-awareness. AgentOps will help AI agents become more aware of their behaviors and act with greater autonomy in managing themselves. For example, future AgentOps will help AI agents evaluate their own behaviors and make self-improvement decisions. Greater predictive capabilities will enable AI agents to anticipate suboptimal behaviors or outcomes, letting AI agents adjust or adapt predictively – before actions are taken.
• Better explainability. AgentOps platforms will embrace standardized approaches for observability, event tracking and compliance. They will also advance in communicating AI agent and system behaviors to human managers, improving the visualization of AI behaviors and decisions. These improvements will further enhance AI security, compliance, bias and discrimination removal and governance efforts.
• Vertical specialization. AgentOps platforms and practices will diversify and specialize to meet the unique needs of niche industries, or verticals, including logistics, healthcare, finance and IT. This is likely to parallel the evolution of vertical AI agents.