Agent-Observability

The dsh trace compare agent exploration use case is the fastest way to see why a DeepSeek Harness agent does what it does: install the dsh-maze plugin (formerly dsh-trace-compare), load a session log or follow a live run, and you get the agent’s full exploration maze — main path, failed detours, and backtracking points — rendered on a single timeline with deterministic, LLM-free analysis you can drill back to the raw commands and outputs. It turns an invisible black-box trajectory into something you can actually read, compare across models, and fix. ...

DeepSeek Harness (dsh) records every agent run as a durable series of session events — reasoning deltas, tool calls, tool results, step starts and ends, and turn boundaries — and dsh trace compare is the workflow for visualizing and contrasting those exploration paths side by side. Instead of staring at linear chat logs, you render agent execution as a graph (a DAG) where parallel tool calls fan out from a single reasoning step and results fan back in, letting you pinpoint exactly where two runs diverge, stall, retry, or branch. This guide walks you through the session-event format, the native trajectory view, community comparison tools, and the pitfalls to avoid. ...

Rungraph is an open-source, zero-instrumentation tool that reads the agent session transcripts already on your disk and renders them as interactive graphs. With a single npx rungraph command, it visualizes the orchestrator, subagents, and tool calls of any Claude Code, Codex, Hermes Agent, opencode, or Cursor run — then lets you ask, in plain language through an MCP server, “what happened?” and lights up the exact nodes the answer refers to. ...

An open source agent harness is the runtime layer that turns a raw LLM into a working agent, handling the execution loop, tool calls, sandboxing, and context management so runs are reproducible. TrueForge is a TypeScript, MIT-licensed harness that delivers this in one process with SQLite locally or Postgres and Redis when hosted, claiming the same accuracy as Claude Managed Agents and deepagents at lower cost. What Is an Agent Harness and Why Does It Matter? Building an agent is easy; running one well is hard. The gap between a prompt that calls a model and a system that reliably completes multi-step tasks is exactly what an agent harness fills. A harness is the software layer that owns the agent execution loop: it decides when to call the model, how to route tool invocations, how to manage the growing context window, and how to keep the whole run safe and auditable. ...

An agent trajectory ETL and compliance router is a pipeline that extracts raw LLM agent traces, transforms them into structured, portable telemetry, and routes each event to the right destination — an audit log, an observability backend, or a data warehouse — based on regulatory and operational rules. It keeps sensitive trajectory data local-first, captures only what is needed, and produces tamper-proof audit trails that satisfy frameworks such as the EU AI Act Article 12 and the NIST AI Risk Management Framework. This guide explains why agent trajectories outgrow general-purpose databases, how to model them, and how to build a compliant, vendor-neutral pipeline. ...

Oodle.ai prices agent trace observability at $10 per million spans, with no sampling, sub-second p99 query latency, and 100% of traces analyzed. That is roughly 8x cheaper than Langfuse’s base tier ($80 per million units) and far below the per-seat-plus-storage model LangSmith uses. The company processed 120 million agent traces in the last month, and its founder reports Langfuse was 6x more expensive for their own observability workload. This review explains how Oodle achieves that price, whether it is genuinely cheap, and who should adopt it. ...

You demo an AI agent to your team. Fifty runs, zero failures. Everyone’s impressed. You deploy to production. Within a week, it’s hallucinating tool calls, getting stuck in loops, and your Slack is full of “the agent did something weird” messages. I’ve been there. Multiple times. And I’ve spent the last year digging into why this happens and what actually works to fix it. The short answer: your agent isn’t broken — your testing methodology is. Single-digit demos and pass/fail judgments hide a massive variance problem that only emerges under statistical scrutiny. Gartner predicts over 40% of AI agent projects will fail by 2027, and in January 2026, a prompt injection in a customer support agent processed a $47,000 fraudulent refund. These aren’t edge cases — they’re systematic failures that most teams aren’t testing for. ...

You demo an AI agent to your team. Fifty runs, zero failures. Everyone’s impressed. You deploy to production. Within a week, it’s hallucinating tool calls, getting stuck in loops, and your Slack is full of “the agent did something weird” messages. I’ve been there. Multiple times. And I’ve spent the last year digging into why this happens and what actually works to fix it. The short answer: your agent isn’t broken — your testing methodology is. Single-digit demos and pass/fail judgments hide a massive variance problem that only emerges under statistical scrutiny. Gartner predicts over 40% of AI agent projects will fail by 2027, and in January 2026, a prompt injection in a customer support agent processed a $47,000 fraudulent refund. These aren’t edge cases — they’re systematic failures that most teams aren’t testing for. ...