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

The growth of autonomous agents through basic models (FMs) such as Large Language Models (LLMs) has revolutionized the way we solve complex, multi-step problems. These agents perform tasks ranging from customer support to software engineering, navigating complex workflows that combine reasoning, tool usage, and memory.

However, as these systems increase in capacity and complexity, observability, reliability and compliance challenges arise.

This is where AgentOps comes into the picture; a concept modeled after DevOps and MLOps, but tailored to lifecycle management of FM-based agents.

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 manufacturing. The objectives are:

• Observability: Provides complete visibility into the agent’s execution and decision-making processes.
• Traceability: Capture detailed artifacts throughout the agent lifecycle for debugging, optimization, and compliance.
• Reliability: Ensure consistent and reliable results through monitoring and robust workflows.

At its core, AgentOps goes 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 huge action space, requiring decisions at every step. This complexity requires advanced planning and monitoring mechanisms.

2. Observability requirements

High-stakes use cases such as medical diagnosis or legal analysis require detailed traceability. Compliance with regulations such as the EU AI Act further underlines the need for robust observation frameworks.

3. Debugging and optimization

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

4. Scalability and cost management

Scaling agents for production requires metrics such as latency, token usage, and operational costs to ensure efficiency without sacrificing quality.

Core features of AgentOps platforms

1. Create and customize agents

Developers can configure agents using a registry of components:

• Roles: Define responsibilities (e.g. researcher, planner).
• Handrails: Set restrictions to ensure ethical and trustworthy behavior.
• Toolbox: Enable integration with APIs, databases or knowledge graphs.

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

2. Observability and tracking

AgentOps records detailed execution logs:

• Tracks: Record every step in the agent’s workflow, from LLM calls to tool usage.
• Spans: Break traces into detailed steps, such as fetching, generating embeds, or invoking tools.
• artifacts: Keep track of intermediate outputs, memory states, and prompt templates to aid debugging.

Observability tools such as Langfuse or Arize provide dashboards that visualize these traces, allowing bottlenecks or errors to be identified.

3. Quick management

Rapid engineering plays an important role in the behavior of forming agents. Key features include:

• Version control: Track iterations of prompts for performance comparison.
• Injection detection: Identify malicious code or input errors within prompts.
• Optimization: Techniques such as Chain-of-Thought (CoT) or Tree-of-Thought improve reasoning skills.

4. Feedback integration

Human feedback remains crucial for iterative improvements:

• Explicit feedback: Users rate the results or comment.
• Implicit feedback: Metrics such as time on task or click-through rates are analyzed to measure 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 in:

• Benchmarks: Compare agent performance against industry standards.
• Step-by-step assessments: Review intermediate steps in workflows to ensure their accuracy.
• Pathway Evaluation: Validate the agent’s decision-making process.

6. Memory and knowledge integration

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

7. Monitoring and statistics

Extensive monitoring tracks:

• Latency: Measure response times for optimization.
• Token usage: Monitor resource consumption to keep costs under control.
• 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 article introduces a systematic taxonomy of artifacts that support AgentOps observability:

• Artifacts for creating agents: Metadata about roles, goals and limitations.
• Execution artifacts: Logs of tool calls, subtask queues, and reasoning steps.
• Evaluation artifacts: Benchmarks, feedback loops and scoring metrics.
• Track artifacts: Session IDs, trace IDs and ranges for detailed monitoring.

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

AgentOps (tool) Walkthrough

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

Step 1: Install the AgentOps SDK

Install AgentOps using your favorite Python package manager:

pip install agentops

Step 2: Initialize AgentOps

First, import AgentOps and initialize it with your API key. Save the API key in a .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 observability for all LLM interactions in your application.

Step 3: Register promotions with decorators

You can instrument specific functions using the @record_action decorator, who keeps track of 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 feature is now logged in the AgentOps dashboard and provides metrics for execution time and input-output tracking.

Step 4: Follow named agents

If you use named agents, use the @track_agent decorator to link 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)

All actions or LLM calls within this agent are now linked to the "math-agent" label.

Step 5: Multi-agent support

For systems that use multiple agents, you can track events between agents for better observation. 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 appears in the AgentOps dashboard under that agent’s trace.

Step 6: End the session

To indicate the end of a session, use the end_session method. Optionally add the session status (Success or Fail) and a reason.

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

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

Step 7: Visualize in AgentOps Dashboard

Visit AgentOps dashboard explore:

• Session Replays: Step-by-step execution traces.
• Analysis: LLM fees, token usage and latency metrics.
• Error detection: Identify and debug errors or recursive loops.

Improved example: recursive thought detection

AgentOps also supports detecting recursive loops in agent workflows. Let’s extend the previous example with recursive detection: