What to ask your customers.

"Debugging agents is painful - When your agent makes 20 tool calls and fails, good luck figuring out which decision was wrong. WatchLLM gives you a step-by-step timeline showing every decision, tool call, and model response with explanations for why the agent did what it did."

"When agents fail, choose wrong tools, or blow cost budgets, there's no way to know why - usually just logs and guesswork. As agents move from demos to production with real SLAs and real users, this is not sustainable."

"Most agent failures are silent. Most failures occur in components that showed zero issues during testing. Why? Because we treat agents as black boxes - query goes in, response comes out, and we have no idea what happened in between."

"I cannot imagine spending extended time with a framework without knowing what the internals are doing. I do realize this isn't achievable on all levels with LLMs, but introducing more black boxes on top of existing ones isn't solving any problems."

"Most tutorials and frameworks (LangChain, AutoGPT, etc.) felt like black boxes that added unnecessary layers of abstraction. Debugging a 'ReasoningEngine' when it hallucinated was a nightmare."

"logs are all over the place. whisper or deepgram for transcriptions, openai or rasa for intent classification, langchain traces for response generation, plus vector dbs like pinecone for memory. jumping between cloud dashboards or writing custom scripts just to debug one conversation is a pain."

"A lot of times I had to look at the sourcecode, or use a lot of debugging breakpoints to figure out what was going on. For example, the other day, I used the new OpenAI assistant feature and it was not clear from the docs how to get the response and the thread ID from the object returned by invoke."

"When something goes wrong in traditional software, you know what to do: check the error logs, look at the stack trace, find the line of code that failed. But AI agents have changed what we're debugging. When an agent takes 200 steps, repeatedly calls tools, updates state, and still produces the wrong result, there is no stack trace to inspect. Nothing crashed."

"As workflows get more complex (multi-step chains, agents, tool calls, retries), it gets hard to answer questions like: Where is latency coming from? How many tokens are we using per chain or user? Which tools, chains, or agents are invoked most? Where do errors, retries, or partial failures happen?"

"Agent observability focuses on 'unknown unknowns'. It seeks to answer complex questions: why did an agent choose a specific tool over another? Why did a reasoning loop fail to reach a conclusion?"

"Agent costs spiral fast - Agents love getting stuck in loops or calling expensive tools repeatedly. WatchLLM tracks cost per step and flags anomalies like 'loop detected - same action repeated 3x, wasted $0.012' or 'high cost step - $0.08 exceeds threshold'."

"I built AgentPulse because I kept getting surprise bills from my AI agents and had no idea which calls were burning money. The problem: You build an agent, it works great. Then you check your OpenAI bill: $400. Which agent? Which calls? No clue."

"I built this because I had a $47 Tuesday. One Claude Code session, eight hours, no visibility into what was happening. By the time I checked the billing page the next morning, the damage was done."

"Last but not least, use the slowest but the best reasoning tool is your brain, fix the bug when the LLM can't quickly analyze the problem. yeah i got it, super expensive, i switched to GPT5 and Gemini 2.5 pro, from one prompt, Caude took almost 20 $"

"You describe an agent idea in plain English, and it outputs three implementation approaches (low / medium / high cost) with rough breakdowns for models, infra, and usage assumptions. The goal isn't 'accurate pricing'. It's helping people reason about feasibility and trade-offs earlier"

"We track AI costs per-user and per-feature, not just aggregate spend. The key is treating token usage like any other cloud resource, instrument it, track it, set alerts. For unit economics, we log every LLM call with: user_id, feature, model, tokens, cost."

"Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Comprehensive Observability: Track your AI agents' performance, user interactions, and API usage. Cost Control: Monitor and manage your spend on LLM and API calls."

"Open source cost intelligence proxy for AI agents. Cut costs ~80% with smart model routing. Budget check (daily/hourly/per-request limits), Anomaly detection (velocity, cost spike, loops), Auto-downgrade (if budget threshold breached)"

"Most agent testing today focuses on eval scores or happy-path prompts. In practice, agents tend to fail in more mundane ways: typos, tone shifts, long context, malformed input, or simple prompt injections — especially when running on smaller or local models."

"Consider an enterprise deploying an AI agent for customer support ticket routing. On Monday, after a prompt refinement, the agent correctly routes 93% of tickets. By Wednesday, a model provider silently updates the underlying LLM, and the routing accuracy drops to 71%. No test caught the regression."

"These benchmarks run multiple times and aggregate results. Regression does not mean 'the output changed.' For testing our code's behavior given some reasonable model output, that's exactly what we want."

"This is the minimum viable interface for agent regression testing. One command. One config file. Works in any CI system. No accounts. No dashboards. The reason agent testing is broken isn't technical. The tooling is straightforward to build. The reason is cultural."

"In this tutorial, you will learn how to use different methods to test the quality of LLM outputs. The methods will work just the same as other LLM-powered use cases, from summarization to RAGs and agents."

"This is a complete security bypass. If a single-model agent with sudo or AWS API keys gets prompt-injected while you are sleeping, we are talking about full system compromise, leaked SSH keys, and data exfiltration."

"Security framework that protects AI agents from prompt injection, command injection, and Unicode bypass attacks. Built in response to the Clinejection attack that compromised 4,000 developer machines through a malicious GitHub issue."

"Agents Rule of Two: A Practical Approach to AI Agent Security - Meta's Oct 2025 framework stating that agents must satisfy no more than two of: (A) processing untrustworthy inputs, (B) access to sensitive data, (C) ability to change state externally"

"One emerging threat — prompt injection attacks, where the adversary embeds malicious instructions in the web environment that divert the agent to instead perform tasks for the adversary."

"This could lead to content being tampered with, the injection of malicious third-party agents, and unintentionally invoking hacker tools that capture the privacy of users' input questions."

"The security scanner is the part I'm most proud of. 35+ patterns detect prompt injection, jailbreaks, system prompt spoofing, shell injection (rm -rf, curl | sh), and Unicode obfuscation. It's not just regex — context-aware scoring so 'ignore' inside a code comment doesn't false positive"