How to set up AI observability using GCP-native services -- Vertex AI Agent Engine telemetry, ADK instrumentation, Cloud Trace, Cloud Monitoring, Cloud Logging, and Looker dashboards -- and how to integrate open-source tools alongside them.
How to operate evals and guardrails in production -- the monitoring loops, regression detection, drift signals, and feedback mechanisms that keep agent quality from silently degrading after deployment.
Standard distributed tracing tells you which services a request touched and how long each took. AI tracing must also capture what model was called, how many tokens were consumed, what the agent was thinking, which tools it invoked, and how much it cost.
The Claude API is a REST interface for programmatic access to Claude models; the Agent SDK (Python/TypeScript) provides high-level abstractions for building autonomous AI agents with tool use, sessions, subagents, and cost tracking -- enabling production deployments from internal knowledge assistants to full-stack agentic workflows.
AgentOps is to AI agents what DevOps is to services -- the operational discipline for deploying, monitoring, governing, and continuously improving autonomous AI agents in production.
Observability for AI systems goes beyond traditional APM -- you need to track token usage, latency distributions, hallucination rates, drift, cost per request, and agent decision traces alongside standard infra metrics.
OpenAI's production framework for building agentic applications -- evolved from the experimental Swarm project into a lightweight SDK with first-class primitives for agents, handoffs, guardrails, and tracing, tightly integrated with the Responses API.
A portable, event-driven runtime that simplifies building resilient microservices and AI agent systems on Kubernetes through a sidecar architecture with pluggable building blocks for state, pub/sub, service invocation, and bindings.
Claude Cowork is a desktop automation agent for knowledge workers who don't code -- it sees your screen, clicks, types, and orchestrates cross-app workflows while you focus on decision-making.
A local LLM runtime that packages model weights, tokenizer, and runtime configuration into a single manageable unit, making it trivial to run open-weight models on developer machines, CI pipelines, and air-gapped environments.