Google Cloud Agent Observability

Datadog Agent Observability is an AI-agent observability product for evaluating, improving, and tracing agents across development and production. It combines offline experimentation with production observability: teams can evaluate agents in production, build golden datasets from annotated traces, and test changes against those datasets before release. The same Datadog tracer can be used from development through production, with tracing across requests, services, agent reasoning, upstream and downstream dependencies, and end-user experience. It also provides alerting, role-based access control, sensitive-data protection, and HIPAA compliance controls. The product addresses agent quality, cost, safety, and operational behavior in addition to conventional application signals such as latency, errors, traffic, and saturation.

AgentOps is an open-source observability and developer-tools platform for building, evaluating, and monitoring AI agents. Its Python SDK records agent sessions and LLM calls for dashboard-based session replays, step-by-step execution graphs, debugging, analytics, cost tracking, and benchmarking. Developers instrument code with decorators for sessions, agents, operations, tasks, and workflows; these support nested span hierarchies, input and output recording, exception handling, asynchronous functions, generators, and custom attributes. The SDK provides integrations with agent frameworks and LLM systems including CrewAI, AG2/AutoGen, Agno, LangGraph, and the OpenAI Agents SDK, and the platform can be self-hosted with its dashboard and API backend. The app is released under the MIT license.

Google ADK is an open-source framework for building, debugging, evaluating, and deploying AI agents and multi-agent systems. It supports Python, TypeScript, Go, Java, and Kotlin, and provides agent definitions with prompts, model calls, tools, and multi-agent orchestration. Its graph workflows combine deterministic code with adaptive AI reasoning through structured execution paths. ADK manages agent context by assembling sessions, memory, tool outputs, and artifacts, filtering irrelevant events, summarizing older turns, lazy-loading artifacts, and tracking token usage. Agents can run on self-managed infrastructure or deploy to Google Cloud through Agent Runtime, Cloud Run, or GKE. In the cited demo, ADK agents were orchestrated inside a Temporal workflow for a human-in-the-loop ice cream delivery process.

A built-in tool for Google's agent API that a model can call autonomously during an agent run; the supplied video describes it in a CVE-agent demonstration.

Vertex AI is Google Cloud’s managed platform for developing and deploying machine-learning and generative-AI applications. It provides hosted models and APIs that applications, including agents, can call at runtime, along with tools for model training, evaluation, and serving.