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AI Agents: From Prototype to Production with SpringAI and MCP

This item discusses the challenges of deploying AI agents in production, focusing on handling failures, outages, prompt injections, and traffic spikes. It highlights the importance of guardrails and observability in transforming AI prototypes into reliable production systems. The discussion features insights from Mihaela Gheorghe-Roman, with a focus on solutions provided by SpringAI and MCP. AI

IMPACT Focuses on practical challenges and solutions for deploying AI agents in production environments, emphasizing reliability and safety.

RANK_REASON The item discusses tools and techniques for deploying AI agents, not a new AI model release or significant industry event.

Read on Mastodon — fosstodon.org →

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AI Agents: From Prototype to Production with SpringAI and MCP

COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    Your # AI agent works in a demo. But can it handle failures, outages, prompt injections & traffic? Join Mihaela Gheorghe-Roman to learn how # SpringAI , # MCP ,

    Your # AI agent works in a demo. But can it handle failures, outages, prompt injections & traffic? Join Mihaela Gheorghe-Roman to learn how # SpringAI , # MCP , guardrails & observability turn prototypes into production systems: https:// javapro.io/2026/06/25/from-pro totype-to-p…