Observability

An agent trajectory monitor watches the full sequence of steps, tool calls, decisions, and token spend an AI agent makes in real time — not just the final answer. Because AI agents consume 5-30x more tokens per task than standard chatbots, real-time trajectory monitoring has moved from a nice-to-have to a budget requirement. It lets you see whether your agent is doing the right work or just getting the right answer by chance. ...

DeepSeek Harness observability usually means standing up an OpenTelemetry collector, a tracing backend, and code instrumentation. The TencentCloud AgentObs SDK for DSH eliminates all of that: it is a DeepSeek Harness plugin that observes the harness’s native session, agent-loop, LLM-stream, and tool lifecycles and uploads GenAI trace spans directly to Tencent Cloud Log Service (CLS) using Protobuf. There is no OTLP collector, no sidecar, and no extra service to deploy. If you already run agents on Tencent Cloud, this is the fastest path from “where did my agent spend its tokens?” to a dashboards-and-alerts answer. ...

OpenTelemetry tracing for DeepSeek Harness lets you export every agent session, LLM call, and tool invocation as a standard OTLP trace tree to backends like Jaeger, Grafana Tempo, SigNoz, or Langfuse. You add it by installing a community plugin that implements the official @deepseek-ai/dsh-session-telemetry seam, configure an OTLP endpoint and a privacy mode, and then read the GenAI trace tree to debug agent loops, retries, and token usage. What is DeepSeek Harness and why it needs tracing DeepSeek Harness is the official open-source agent framework from DeepSeek, written in TypeScript with the tagline “Everything is a Plugin.” Its official repository has roughly 123,000 GitHub stars, making it one of the most popular agent harnesses in the ecosystem. The framework orchestrates multi-step agent loops: it plans, calls LLMs, invokes tools, spawns subagents, and retries failed steps. Each of those steps is a potential failure point, and without tracing you are effectively debugging a black box. ...

What is mcpsnoop? — Wireshark for MCP mcpsnoop is an open-source transparent proxy for the Model Context Protocol (MCP) that sits between your MCP client and server, capturing every JSON-RPC frame in real time. Built in Go with a Bubble Tea terminal UI, it gives developers the same kind of deep packet inspection for MCP traffic that Wireshark provides for network packets. Launched in late June 2026, mcpsnoop reached 270 GitHub stars in just three weeks and is rapidly becoming the go-to tool for MCP debugging. ...

An AI agent overspend model line mistake is a configuration bug with a billing blast radius. In my case, a missing model value silently routed routine agent steps to a pro-tier model, and the fastest fix was not prompt tuning. It was tracing requested_model, response_model, tokens, tools, retries, and config diffs in one place. What actually happened when the model line was missing? The failure was boring, which is why it was expensive. ...

OpenTelemetry is the standard way to add structured tracing, metrics, and logs to AI agents in 2026 — covering token usage, tool call latency, and multi-agent context propagation with a single SDK and vendor-neutral backends. Why Traditional Observability Fails for AI Agents Traditional APM tools like Datadog APM or New Relic were designed for deterministic request/response cycles: a user hits an endpoint, a function runs, a database query fires, a response returns. The execution path is fixed, latency is bounded, and errors are binary. AI agents break every one of these assumptions. An agent reasoning chain is non-deterministic — the same input prompt can trigger three tool calls in one run and seven in the next. Execution duration ranges from 500ms for a fast LLM call to 3+ minutes for a multi-step agent that searches the web, queries a database, and synthesizes results. Without agent-native spans, you cannot tell which tool call caused a timeout or why a particular run cost $0.40 while a similar one cost $0.03. Traditional APM measures function latency in microseconds and ignores tokens entirely. The LLM observability platform market recognized this gap — growing to an estimated $2.69 billion in 2026 and projected to reach $9.26 billion by 2030 at a 36.2% CAGR. OpenTelemetry’s GenAI Semantic Conventions fill that gap with a purpose-built span model for LLM operations, agent reasoning loops, and tool executions that traditional APM never anticipated. ...

The fastest-moving part of AI infrastructure in 2026 is observability — and for good reason. The LLM observability platform market hit $2.69B this year (up from $1.97B in 2025), growing at a 36.3% CAGR. Three platforms dominate production use: Braintrust (SaaS-only, $80M Series B, enterprise-grade CI/CD gates), Arize Phoenix (100% open-source, OpenTelemetry-native, 9,100+ GitHub stars), and Langfuse (MIT-licensed, ClickHouse-acquired, 19,000+ GitHub stars). Choosing the wrong one means either paying for features you won’t use or hitting invisible ceilings when your agent fleet scales. ...