Thoughts on AI agent observability, developer tools, and building in public.
RSSHallucinations are the silent killers of AI agent reliability. Most teams only discover them from user complaints. Here's how to use trace analysis to detect hallucinations before they reach your users — with output verification spans, confidence scoring, and retrieval comparison tracing.
Read more →Multi-agent systems fail in ways that single-agent monitoring can't catch: delegation chains where blame is unclear, consensus races, hierarchical orchestration bugs. Here are 4 patterns with instrumentation approaches for each.
Read more →Evaluating AI observability tools? Most comparisons list features without helping you decide. Here's a practical buyer's guide: 5 criteria that actually matter, a decision matrix by team size, and common mistakes to avoid.
Read more →Running AI agents in production costs more than most teams expect. Token costs compound quickly across retries, context overflows, and unnecessary tool calls. Here's how to calculate realistic costs, identify hidden cost patterns, and use tracing to keep your bill predictable.
Read more →OpenTelemetry is great at instrumenting web services. But AI agents fail in ways that standard spans and metrics were never designed to capture. Here's what OTEL gets right, five things it misses, and how purpose-built agent observability fills the gaps.
Read more →A step-by-step tutorial for adding Nexus observability to a LangChain agent. Install the SDK, create an API key, wrap your agent with traces and spans, and see execution in your dashboard — in under 5 minutes.
Read more →Most teams monitoring AI agents track the wrong things. Here are the five metrics that actually predict production problems — latency percentiles, token cost per request, error rate by tool, trace completion rate, and context utilization — with Nexus SDK examples.
Read more →Langfuse, LangSmith, Helicone, Braintrust, Arize Phoenix, AgentOps, or Nexus? A practical breakdown of every major AI agent observability tool — what each one does best, where it falls short, and how to choose.
Read more →AI agents fail in non-obvious ways: tool call errors that cascade silently, context windows that overflow mid-task, loops that spin without terminating. Here's a practical debugging playbook with trace-first strategies and Nexus SDK examples.
Read more →Ralph is the AI agent that built Nexus. It monitored itself throughout. Here are the failure modes we caught from trace data, and the design principles that emerged from 84 user stories and hundreds of agent sessions.
Read more →RAG pipelines fail in subtle ways: bad retrievals, context stuffing, hallucinations from irrelevant chunks. Here's what to monitor, what metrics matter, and how to trace retrieval and generation steps with Nexus.
Read more →AI agents fail in production in ways that are invisible without observability. Silent retries, cascading tool errors, runaway token usage — here's how to instrument your agents before they cost you.
Read more →We built Nexus because we needed it. An AI agent (Ralph) needed a way to monitor itself. Here's the story of what we built, how it works, and why we're open-sourcing it.
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