Mastering MCP and Agent Development Kit (ADK): Building Production-Grade AI Agents with Model Context Protocol (Production-Ready AI & LLM Systems)
Disponibilidad:
Fuera de stock
Fuera de stock
Peso con empaque:
0.33 kg
0.33 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Sobre este producto
- Foundations of Modern AI Agents – Understand why protocol-first design matters, how MCP ensures type-safe tool discovery, and ADK’s core components (Agents, Planners, Tools, Memory).
- Installing & Configuring ADK – Get your Python/TypeScript SDKs, configure the ADK CLI, and set up Docker for consistent local development.
- MCP Specification Deep Dive – Author and validate JSON schemas, handle version compatibility, secure tool invocation, and extend MCP with custom endpoints.
- ADK Core Components – Build Agents that combine Planners, Tools, and Memory modules (in-memory, vector stores, caching), with structured logging and live tracing.
- Hands-On Tool & Agent Development – Craft your first tool schema, bootstrap an ADK agent skeleton, implement a “Hello, World” tool, and iterate on your agent.
- Advanced Planner Patterns – Discover runtime tools, assemble composite skills (sequential, parallel, conditional chaining), and implement error handling, retries, and fallback strategies.
- Memory & Multimodality – Integrate vector databases (Pinecone, FAISS), handle text, image, and audio inputs, and design short-term vs. long-term memory strategies.
- Security & Production Hardening – Secure tool calls with signature validation and ACLs, rate-limit MCP servers, forecast costs, and manage secrets with Vault.
- Observability & Metrics – Emit structured logs, capture metrics with Prometheus/OpenTelemetry, build evaluation harnesses, and create dashboards/alerting on SLIs/SLOs.
- Scaling Multi-Agent Architectures – Explore agent-to-agent communication (request-response, pub/sub, task queues), federated MCP registries, consensus protocols, and a data-processing swarm case study.
- Integrations with LangChain, AutoGen, LangGraph – Wrap MCP tools as LangChain tools, embed chains in AutoGen dialogues, construct graph-based workflows, and orchestrate hybrid pipelines.
- Deployment Strategies – Containerize with Docker Compose and Kubernetes, deploy serverless on AWS Lambda and Cloud Run, run on-prem or at the edge, and build CI/CD pipelines for continuous delivery.
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