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AI Agents in Production: A Practical Guide to Building Reliable LLM-Powered Systems with Claude, GPT, and MCP
Paperback
Series: Production AI Engineering
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ISBN13: 9798197313966
Publisher: Independently Published
Pages: 80
Weight: 0.26
Height: 0.17 Width: 6.00 Depth: 9.00
Language: English
Publisher: Independently Published
Pages: 80
Weight: 0.26
Height: 0.17 Width: 6.00 Depth: 9.00
Language: English
Stop Building Demos. Start Shipping Production-Ready AI Agents.
AI agents are transforming software engineering, but deploying a reliable system that runs at scale is entirely different from running a local prototype. AI Agents in Production is your definitive, hands-on guide to taking large language model (LLM) applications from fragile experiments to robust, scalable enterprise systems. Written specifically for working software engineers, this book provides the practical blueprints you need to build reliable agentic systems using Claude, GPT, and the Model Context Protocol (MCP).
AI agents are transforming software engineering, but deploying a reliable system that runs at scale is entirely different from running a local prototype. AI Agents in Production is your definitive, hands-on guide to taking large language model (LLM) applications from fragile experiments to robust, scalable enterprise systems. Written specifically for working software engineers, this book provides the practical blueprints you need to build reliable agentic systems using Claude, GPT, and the Model Context Protocol (MCP).
What You Will Learn
- Design Robust Architectures: Master ReAct, Plan-and-Execute, and advanced multi-agent system patterns.
- Integrate Real-World Tools: Use the Model Context Protocol (MCP) to seamlessly connect agents to external APIs with Python and Node.
- Implement Bulletproof Reliability: Master advanced error handling, retry logic, and graceful degradation for LLM variability.
- Instrument for Observability: Track traces, metrics, and agent behavior logs to monitor your systems in real-time.
- Defend Against Vulnerabilities: Secure your agents against prompt injection, unauthorized tool access, and model drift.
- Scale for the Enterprise: Optimize throughput and cost using message queues, parallelism, and efficient context memory management.
- Automate Evaluation: Build continuous regression testing and quality evaluation pipelines for non-deterministic AI.
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