#agentops

We all remember the early days of cloud computing. It was a golden age of scalability and promises of simplified infrastructure. But as our cloud footprints grew, so did the complexity. Monitoring, security, deployment and it all started feeling a bit overwhelming.

We all remember the early days of cloud computing. It was a golden age of scalability and promises of simplified infrastructure. But as our cloud footprints grew, so did the complexity. Monitoring, security, deployment and it all started feeling a bit overwhelming.

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The Rise of LLMOps in the Age of AI

In the fast-evolving IT landscape, MLOps—short for Machine Learning Operations—has become the secret weapon for organizations aiming to turn complex data into powerful, actionable insights. MLOps is a set of practices designed to streamline the machine learning (ML) lifecycle—helping data scientists, IT teams, business stakeholders, and domain experts collaborate to build, deploy, and manage ML models consistently and reliably. It emerged to address challenges unique to ML, such as ensuring data quality and avoiding bias, and has become a standard approach for managing ML models across business functions.

With the rise of large language models (LLMs), however, new challenges have surfaced. LLMs require massive computing power, advanced infrastructure, and techniques like prompt engineering to operate efficiently. These complexities have given rise to a specialized evolution of MLOps called LLMOps (Large Language Model Operations).

LLMOps focuses on optimizing the lifecycle of LLMs, from training and fine-tuning to deploying, scaling, monitoring, and maintaining models. It aims to address the specific demands of LLMs while ensuring they operate effectively in production environments. This includes management of high computational costs, scaling infrastructure to support large models, and streamlining tasks like prompt engineering and fine-tuning.

With this shift to LLMOps, it’s important for business and IT leaders to understand the primary benefits of LLMOps and determine which process is most appropriate to utilize and when.

Key Benefits of LLMOps

LLMOps builds upon the foundation of MLOps, offering enhanced capabilities in several key areas. The top three ways LLMOps deliver greater benefits to enterprises are:

Democratization of AI – LLMOps makes the development and deployment of LLMs more accessible to non-technical stakeholders. In traditional ML workflows, data scientists primarily handle model building, while engineers focus on pipelines and operations. LLMOps shifts this paradigm by leveraging open-source models, proprietary services, and low-code/no-code tools. These tools simplify model building and training, enabling business teams, product managers, and engineers to collaborate more effectively. Non-technical users can now experiment with and deploy LLMs using intuitive interfaces, reducing the technical barrier to AI adoption.

Faster Model Deployment: LLMOps streamlines the integration of LLMs with business applications, enabling teams to deploy AI-powered solutions more quickly and adapt to changing market demands. For example, with LLMOps, businesses can rapidly adjust models to reflect customer feedback or regulatory updates without extensive redevelopment cycles. This agility ensures that organizations can stay ahead of market trends and maintain a competitive edge.

Emergence of RAGs – Many enterprise use cases for LLMs involve retrieving relevant data from external sources rather than relying solely on pre-trained models. LLMOps introduces Retrieval-Augmented Generation (RAG) pipelines, which combine retrieval models to fetch data from knowledge bases with LLMs that rank and summarize the information. This approach reduces hallucinations and offers a cost-effective way to leverage enterprise data. Unlike traditional ML workflows, where model training is the primary focus, LLMOps shifts attention to building and managing RAG pipelines as a core function in the development lifecycle.

Importance of understanding LLMOps use cases

With the general benefits of LLMOps, including the democratization of AI tools across the enterprise, it’s important to look at specific use cases where LLMOps can be introduced to help business leaders and IT teams better leverage LLMs:

Safe deployment of models– Many companies begin their LLM development with internal use cases, including automated customer support bots or code generation and review to gain confidence in LLM performance before scaling to customer-facing applications. LLMOps frameworks help teams streamline a phased rollout of these use cases by 1) automating deployment pipelines that isolate internal environments from customer-facing ones, 2) enabling controlled testing and monitoring in sandboxed environments to identify and address failure modes, and 3) supporting version control and rollback capabilities so teams can iterate on internal deployments before going live externally.

Model risk management – LLMs alone introduce increased concerns around model risk management, which has always been a critical focus for MLOps. Transparency into what data LLMs are trained on is often murky, raising concerns about privacy, copyrights, and bias. Data hallucinations have been a huge pain point in the development of models. However, with LLMOps this challenge is addressed. LLMOps are able to monitor model behavior in real-time, enabling teams to 1) detect and register hallucinations using pre-defined shortcuts, 2) implement feedback loops to continuously refine the models by updating prompts or retraining with corrected outputs, and 3) utilize metrics to better understand and address generative unpredictability.

Evaluating and monitoring models– Evaluating and monitoring standalone LLMs is more complex than with traditional standalone ML models. Unlike traditional models, LLM applications are often context-specific, requiring input from subject matter experts for effective evaluation. To address this complexity, auto-evaluation frameworks have emerged, where one LLM is used to assess another. These frameworks create pipelines for continuous evaluation, incorporating automated tests or benchmarks managed by LLMOps systems. This approach tracks model performance, flags anomalies, and improves evaluation criteria, simplifying the process of assessing the quality and reliability of generative outputs.

LLMOps provides the operational backbone to manage the added complexity of LLMs that MLOps cannot manage by itself. LLMOps ensures that organizations can tackle pain points like the unpredictability of generative outputs and the emergence of new evaluation frameworks, all while enabling safe and effective deployments. With this, it’s vital that enterprises understand this shift from MLOps to LLMOps in order to address LLMs unique challenges within their own organization and implement the correct operations to ensure success in their AI projects.

Looking ahead: embracing AgentOps

Now that we’ve delved into LLMOps, it’s important to consider what lies ahead for operation frameworks as AI continuously innovates. Currently at the forefront of the AI space is agentic AI, or AI agents – which are fully automated programs with complex reasoning capabilities and memory that uses an LLM to solve problems, creates its own plan to do so, and executes that plan. Deloitte predicts that 25% of enterprises using generative AI are likely to deploy AI agents in 2025, growing to 50% by 2027. This data presents a clear shift to agentic AI in the future – a shift that has already begun as many organizations have already begun implementing and developing this technology.

With this, AgentOps is the next wave of AI operations that enterprises should prepare for.

AgentOps frameworks combine elements of AI, automation, and operations with the goal of improving how teams manage and scale business processes. It focuses on leveraging intelligent agents to enhance operational workflows, provide real-time insights, and support decision-making in various industries. Implementing AgentOps frameworks significantly enhances the consistency of an AI agent’s behaviour and responses to unusual situations, aiming to minimize downtime and failures. This will become necessary as more and more organizations begin deploying and utilizing AI agents within their workflows.

AgentOps is a necessity component for managing the next generation of AI systems. Organizations must focus on ensuring the system’s observability, traceability, and enhanced monitoring to develop innovative and forward-thinking AI agents. As automation advances and AI responsibilities grow, the effective integration of the AgentOps is essential for organizations to maintain trust in AI and scale intricate, specialized operations.

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Autonomous Agents with AgentOps: Observability, Traceability, and Beyond for your AI Application

The growth of autonomous agents by foundation models (FMs) like Large Language Models (LLMs) has reform how we solve complex, multi-step problems. These agents perform tasks ranging from customer support to software engineering, navigating intricate workflows that combine reasoning, tool use, and memory.

However, as these systems grow in capability and complexity, challenges in observability, reliability, and compliance emerge.

This is where AgentOps comes in; a concept modeled after DevOps and MLOps but tailored for managing the lifecycle of FM-based agents.

To provide a foundational understanding of AgentOps and its critical role in enabling observability and traceability for FM-based autonomous agents, I have drawn insights from the recent paper A Taxonomy of AgentOps for Enabling Observability of Foundation Model-Based Agents by Liming Dong, Qinghua Lu, and Liming Zhu. The paper offers a comprehensive exploration of AgentOps, highlighting its necessity in managing the lifecycle of autonomous agents—from creation and execution to evaluation and monitoring. The authors categorize traceable artifacts, propose key features for observability platforms, and address challenges like decision complexity and regulatory compliance.

While AgentOps (the tool) has gained significant traction as one of the leading tools for monitoring, debugging, and optimizing AI agents (like autogen, crew ai), this article focuses on the broader concept of AI Operations (Ops).

That said, AgentOps (the tool) offers developers insight into agent workflows with features like session replays, LLM cost tracking, and compliance monitoring. As one of the most popular Ops tools in AI,  later on the article we will go through its functionality with a tutorial.

What is AgentOps?

AgentOps refers to the end-to-end processes, tools, and frameworks required to design, deploy, monitor, and optimize FM-based autonomous agents in production. Its goals are:

Observability: Providing full visibility into the agent’s execution and decision-making processes.

Traceability: Capturing detailed artifacts across the agent’s lifecycle for debugging, optimization, and compliance.

Reliability: Ensuring consistent and trustworthy outputs through monitoring and robust workflows.

At its core, AgentOps extends beyond traditional MLOps by emphasizing iterative, multi-step workflows, tool integration, and adaptive memory, all while maintaining rigorous tracking and monitoring.

Key Challenges Addressed by AgentOps

1. Complexity of Agentic Systems

Autonomous agents process tasks across a vast action space, requiring decisions at every step. This complexity demands sophisticated planning and monitoring mechanisms.

2. Observability Requirements

High-stakes use cases—such as medical diagnosis or legal analysis—demand granular traceability. Compliance with regulations like the EU AI Act further underscores the need for robust observability frameworks.

3. Debugging and Optimization

Identifying errors in multi-step workflows or assessing intermediate outputs is challenging without detailed traces of the agent’s actions.

4. Scalability and Cost Management

Scaling agents for production requires monitoring metrics like latency, token usage, and operational costs to ensure efficiency without compromising quality.

Core Features of AgentOps Platforms

1. Agent Creation and Customization

Developers can configure agents using a registry of components:

Roles: Define responsibilities (e.g., researcher, planner).

Guardrails: Set constraints to ensure ethical and reliable behavior.

Toolkits: Enable integration with APIs, databases, or knowledge graphs.

Agents are built to interact with specific datasets, tools, and prompts while maintaining compliance with predefined rules.

2. Observability and Tracing

AgentOps captures detailed execution logs:

Traces: Record every step in the agent’s workflow, from LLM calls to tool usage.

Spans: Break down traces into granular steps, such as retrieval, embedding generation, or tool invocation.

Artifacts: Track intermediate outputs, memory states, and prompt templates to aid debugging.

Observability tools like Langfuse or Arize provide dashboards that visualize these traces, helping identify bottlenecks or errors.

3. Prompt Management

Prompt engineering plays an important role in forming agent behavior. Key features include:

Versioning: Track iterations of prompts for performance comparison.

Injection Detection: Identify malicious code or input errors within prompts.

Optimization: Techniques like Chain-of-Thought (CoT) or Tree-of-Thought improve reasoning capabilities.

4. Feedback Integration

Human feedback remains crucial for iterative improvements:

Explicit Feedback: Users rate outputs or provide comments.

Implicit Feedback: Metrics like time-on-task or click-through rates are analyzed to gauge effectiveness.

This feedback loop refines both the agent’s performance and the evaluation benchmarks used for testing.

5. Evaluation and Testing

AgentOps platforms facilitate rigorous testing across:

Benchmarks: Compare agent performance against industry standards.

Step-by-Step Evaluations: Assess intermediate steps in workflows to ensure correctness.

Trajectory Evaluation: Validate the decision-making path taken by the agent.

6. Memory and Knowledge Integration

Agents utilize short-term memory for context (e.g., conversation history) and long-term memory for storing insights from past tasks. This enables agents to adapt dynamically while maintaining coherence over time.

7. Monitoring and Metrics

Comprehensive monitoring tracks:

Latency: Measure response times for optimization.

Token Usage: Monitor resource consumption to control costs.

Quality Metrics: Evaluate relevance, accuracy, and toxicity.

These metrics are visualized across dimensions such as user sessions, prompts, and workflows, enabling real-time interventions.

The Taxonomy of Traceable Artifacts

The paper introduces a systematic taxonomy of artifacts that underpin AgentOps observability:

Agent Creation Artifacts: Metadata about roles, goals, and constraints.

Execution Artifacts: Logs of tool calls, subtask queues, and reasoning steps.

Evaluation Artifacts: Benchmarks, feedback loops, and scoring metrics.

Tracing Artifacts: Session IDs, trace IDs, and spans for granular monitoring.

This taxonomy ensures consistency and clarity across the agent lifecycle, making debugging and compliance more manageable.

AgentOps (tool) Walkthrough

This will guide you through setting up and using AgentOps to monitor and optimize your AI agents.

Step 1: Install the AgentOps SDK

Install AgentOps using your preferred Python package manager:

pip install agentops

Step 2: Initialize AgentOps

First, import AgentOps and initialize it using your API key. Store the API key in an .env file for security:

# Initialize AgentOps with API Key import agentops import os from dotenv import load_dotenv # Load environment variables load_dotenv() AGENTOPS_API_KEY = os.getenv("AGENTOPS_API_KEY") # Initialize the AgentOps client agentops.init(api_key=AGENTOPS_API_KEY, default_tags=["my-first-agent"])

This step sets up observability for all LLM interactions in your application.

Step 3: Record Actions with Decorators

You can instrument specific functions using the @record_action decorator, which tracks their parameters, execution time, and output. Here’s an example:

from agentops import record_action @record_action("custom-action-tracker") def is_prime(number): """Check if a number is prime.""" if number < 2: return False for i in range(2, int(number**0.5) + 1): if number % i == 0: return False return True

The function will now be logged in the AgentOps dashboard, providing metrics for execution time and input-output tracking.

Step 4: Track Named Agents

If you are using named agents, use the @track_agent decorator to tie all actions and events to specific agents.

from agentops import track_agent @track_agent(name="math-agent") class MathAgent: def __init__(self, name): self.name = name def factorial(self, n): """Calculate factorial recursively.""" return 1 if n == 0 else n * self.factorial(n - 1)

Any actions or LLM calls within this agent are now associated with the "math-agent" tag.

Step 5: Multi-Agent Support

For systems using multiple agents, you can track events across agents for better observability. Here’s an example:

@track_agent(name="qa-agent") class QAAgent: def generate_response(self, prompt): return f"Responding to: prompt" @track_agent(name="developer-agent") class DeveloperAgent: def generate_code(self, task_description): return f"# Code to perform: task_description" qa_agent = QAAgent() developer_agent = DeveloperAgent() response = qa_agent.generate_response("Explain observability in AI.") code = developer_agent.generate_code("calculate Fibonacci sequence")

Each call will appear in the AgentOps dashboard under its respective agent’s trace.

Step 6: End the Session

To signal the end of a session, use the end_session method. Optionally, include the session state (Success or Fail) and a reason.

# End of session agentops.end_session(state="Success", reason="Completed workflow")

This ensures all data is logged and accessible in the AgentOps dashboard.

Step 7: Visualize in AgentOps Dashboard

Visit AgentOps Dashboard to explore:

Session Replays: Step-by-step execution traces.

Analytics: LLM cost, token usage, and latency metrics.

Error Detection: Identify and debug failures or recursive loops.

Enhanced Example: Recursive Thought Detection