#dashboard detectives

[netflix]

@adulthoodisokay

[dashboarddiaries]

Our TVs have been graced with @neil-gaiman in recent years, but Tumblr has been graced with him for over a decade! This month we discuss Uncle Neil's influence, as well as Dead Boy Detectives, Bridgerton, Shogun, and taking your coffee orally.

Credits and transcript in our reblog. You can find transcripts for this, and every other episode, here.

Find the posts discussed in this episode in this tag!

[witnessprotectionprogramurl]

dirk gently season 2 dashboard simulator

🔎 holistic-detective Follow

this edible ain't shit

🔎 holistic-detective Follow

there's a house inside the house

🔎 holistic-detective Follow

where's that music coming from

🔎 holistic-detective Follow

ok so I might be in actual judeo-christian hell

18 notes

👾 purplepeopleeater Follow

im so hungry

🔎 holistic-detective Follow

how hungry

🔎 holistic-detective Follow

OP HOW HUNGRY

682 notes

🔪 bart Follow

has anyone seen Ken?

3 notes

✂️ normal-man-from-this-world Follow

I will find dirk gently, fulfill the prophecy, and save Wendimoor!

✂️ normal-man-from-this-world Follow

jail

835 notes

🚖 literally-just-some-guy-deactivated Follow

put your hand in my enclosure I promise I won't bite

🕶 blackwing-boss Follow

hey what's your superpower

🚖 literally-just-some-guy-deactivated Follow

please let me out

🚖 literally-just-some-guy-deactivated Follow

I'll be sooooo niceys if you let me out

411 notes

🕶 blackwing-boss Follow

what a nice day! I sure hope no one manipulates me into giving them the same clearance as me so they can take my job

💻 newer-better-blackwing-boss Follow

>:3

1k notes

🚬 the-fifth-rowdy Follow

fuckkkkk I don't know where my friends are

🚬 the-fifth-rowdy Follow

welp. to the train tracks!

🚬 the-fifth-rowdy Follow

AAAAAAAAAAAAAAAAAAAAAAAAAAA

🚬 the-fifth-rowdy Follow

don't worry guys I'm fine

🧍‍♂️randombystander Follow

no i don't think you are

🚬 the-fifth-rowdy Follow

you've never been plagued by visions and it shows

83k notes

ok i give up theres too many characters and things that happen in that show jesus christ

rip Farah and also Tina and also mona I swear I tried to include them (and also more bart lmao) but I couldn't decide on emojis and/or urls and then I died

lmao I just realized I forgot. todd. ALSO BEAST I DIDNT FORGET HER I JUST GAVE UP BEFORE THINKING OF A POST FOR HER 😭 ok sorry I'll go now

[startswith0]

[prolibytherium]

I'm perpetually confused by whatever algorithm tumblr uses to autohide posts like I just got away with posting a drawing of my OCs like .000005 seconds away from dry humping, with large and prominent areas of skintone, but can't get seven uncolored stylized drawings of animals through

[lemon-3ds]

how. HOW do you have so many hats

- Free to play TF2 players

[livinginadumpster]

turns out the dbd cancelation is doing a really good job of getting me off social media

[lawain-dimensional-heroes]

"...Yeah I've decided, after all nep and Vert being cozy with their other selves I've decided that maybe returning to Gamindustri is just pointless after all."

Wow IF, that's a bit--

"Just because I am Nep's friend doesn't mean I have limits."

[cxpperhead]

Due to his serpent physiology, Copperhead has an exceptionally keen sense of smell. He's able to detect even subtle changes in the air using his tongue, making it a tough endeavour to get the drop on him. This can be as much of a boon as it is a hindrance however - Gotham doesn't have the most pleasant smells, especially the sewers and having such a powerful sense of taste can be awkward depending on how good his relationship with certain people are.

[divinityunleashed]

"..."

"..."

[netflix]

poll via @baddywronglegs

post via @aliteralchicken

[standalonegemstone]

okay, well if you think they're possible bots, tumblr, why don't you block them

[witnessprotectionprogramurl]

back by popular demand (<- lmao yeah sure), dirk gently season 2 dashboard simulator

🌻 livelaughlove-deactivated

*white knuckling the bathroom sink* I'm nice I'm nice I'm nice I'm nice

🛻 bob-boreton-deactivated

me when i lie

6k notes

👑 thefuturequeenofwendimoor Follow

"what's your body count?" usually there isn't a body left when I'm done with someone

👑 thefuturequeenofwendimoor Follow

my pile of dust count is 4 though

👑 thefuturequeenofwendimoor Follow

frog count is 1

26k notes

🍹 bored-bisexual Follow

fuck my stupid baka life I keep missing the charging port on my phone

🔑 sherlock-not-holmes Follow

are you perhaps an alcoholic

🍹 bored-bisexual Follow

no I've been sober for 12 whole minutes

487k notes

🖍 theboyyy-deactivated

fuck you *causes a power surge in the entire city*

🖍 theboyyy-remade Follow

I need you all to know this post had me in a coma for 50 years

3k notes

🪨 beast Follow

me n my bf bibbit <3

🔎 holistic-detective Follow

yes, I'm bibbit now. this is my life. living in the forest. with my girlfriend, beast. who literally has me on a leash. this is fine I'm fine everything's fine.

🪨 beast Follow

hi bibbit

106k notes

still couldn't think of an emoji for farah 😭 I should make more of these theyre fun ok byeeee

[startswith0]

Chapter 521

[southislandwren]

ARRRGGGHHHHHHHHH the freshman wants to TRADE CARS for the weekend.... how do i politely say i would literally kill myself and everyone in a 10 mile radius if i had to let someone other than my mom, dad, and brother drive my car

[jcmarchi]

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

New Post has been published on https://thedigitalinsider.com/autonomous-agents-with-agentops-observability-traceability-and-beyond-for-your-ai-application/

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

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