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OpenAI’s In-house Data Agent (and the Open-Source Alternative) | Dash by Agno
Dash data agent is an open-source self-learning data agent inspired by OpenAI’s in-house data agent. The goal is ambitious but very practical: let teams ask questions in plain English and…
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Routing Traces, Metrics, and Logs for LLM Agents (Pipelines + Exporters) | OpenTelemetry Collector
OpenTelemetry Collector for LLM agents: The OpenTelemetry Collector is the most underrated piece of an LLM agent observability stack. Instrumenting your agent runtime is step 1. Step 2 (the step…
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Lightweight Distributed Tracing for Agent Workflows (Quick Setup + Visibility) | Zipkin
Zipkin for LLM agents: Zipkin is the “get tracing working today” option. It’s lightweight, approachable, and perfect when you want quick visibility into service latency and failures without adopting a…
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Storing High-Volume Agent Traces Cost-Efficiently (OTel/Jaeger/Zipkin Ingest) | Grafana Tempo
Grafana Tempo for LLM agents: Grafana Tempo is built for one job: store a huge amount of tracing data cheaply, with minimal operational complexity. That matters for LLM agents because…
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Debugging LLM Agent Tool Calls with Distributed Traces (Run IDs, Spans, Failures) | Jaeger
Jaeger for LLM agents: Jaeger is one of the easiest ways to see what your LLM agent actually did in production. When an agent fails, the final answer rarely tells…
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LLM Agent Tracing & Distributed Context: End-to-End Spans for Tool Calls + RAG | OpenTelemetry (OTel)
OpenTelemetry (OTel) is the fastest path to production-grade tracing for LLM agents because it gives you a standard way to follow a request across your agent runtime, tools, and downstream…
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Tool Calling Reliability for LLM Agents: Schemas, Validation, Retries (Production Checklist)
Tool calling is where most “agent demos” die in production. Models are great at writing plausible text, but tools require correct structure, correct arguments, and correct sequencing under timeouts, partial…
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Agent Evaluation Framework: How to Test LLM Agents (Offline Evals + Production Monitoring)
If you ship LLM agents in production, you’ll eventually hit the same painful truth: agents don’t fail once-they fail in new, surprising ways every time you change a prompt, tool,…
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Prompt Injection for Enterprise LLM Agents: Threat Model + Defenses (Tool Calling + RAG)
Prompt Injection For Enterprise Llm Agents is one of the fastest ways to turn a helpful agent into a security incident. If your agent uses RAG (retrieval-augmented generation) or can…
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Enterprise Agent Governance: How to Build Reliable LLM Agents in Production
Enterprise Agent Governance is the difference between an impressive demo and an agent you can safely run in production. If you’ve ever demoed an LLM agent that looked magical—and then…