MLOps.community Overcoming Challenges in AI Agent Deployment: The Sweet Spot for Governance and Security // Spencer Reagan // #349
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Dec 5, 2025 Spencer Reagan, R&D lead at Airia, specializes in AI-agent orchestration and data governance for regulated environments. He dives into the complexities of agent deployment, discussing how messy data impacts AI performance and why many AI platforms struggle to scale. Reagan offers insights on monitoring agents' actions, enhancing trust through frequent oversight, and emphasizing automation in marketing and HR. He highlights the importance of dynamic rules and identity management for secure agent operations, sharing practical analogies to improve agent design.
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LLMs Normalize Messy Enterprise Data
- LLMs excel at turning messy, unstructured corpuses (spreadsheets, emails, CRMs) into normalized, usable summaries.
- That normalization unlocks real-time Q&A and consolidated profiles across fragmented systems.
Build Agents You Can Explain To An Intern
- If you can explain a task to an intern in plain language, you can usually build an effective agent for it.
- Start with human-describable jobs rather than inventing complex, ambiguous agent workflows.
Mix Deterministic Logic With LLMs
- Use deterministic software for predictable parts and LLMs for semantic, summarization, or generative parts.
- Don't throw AI at entire problems; pick the tasks where repetition and scale deliver ROI.