--- title: The Production AI Playbook: Deploying Agents at... description: A framework for deploying AI agents in enterprise production environments, built from real-world lessons across regulated industries. The five-pillar approach... canonical: https://daily.dev/posts/the-production-ai-playbook-deploying-agents-at-enterprise-scale-sandipan-bhaumik-databricks-zt6hfyrll twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks | daily.dev og:description: A framework for deploying AI agents in enterprise production environments, built from real-world lessons across regulated industries. The five-pillar approach... og:url: https://daily.dev/posts/the-production-ai-playbook-deploying-agents-at-enterprise-scale-sandipan-bhaumik-databricks-zt6hfyrll og:image: https://api.daily.dev/og/posts/zT6HFyrLL.png og:image:alt: The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks **[AI Engineer](https://daily.dev/sources/aidotengineer)** · 37 min read · 1 upvotes · 0 comments ## Summary A framework for deploying AI agents in enterprise production environments, built from real-world lessons across regulated industries. The five-pillar approach covers: evaluation (defining measurable success metrics and building living test datasets before writing code), observability (tracing every agent decision for debugging and regulatory compliance), data foundation (quality data strategy for both question data and tracking data), multi-agent orchestration patterns (orchestrator-worker, choreography, human-in-the-loop), and governance (audit trails, PII detection, prompt versioning as change management, model change management). A retail banking chatbot case study illustrates the approach: by building evaluation infrastructure first and selecting the model only in week 7 of an 8-week POC, the team achieved 60%+ query deflection and could rapidly diagnose production issues like stale policy documents in a vector database. Key lessons include treating the test case library as a living system, enforcing meaningful prompt commit messages, and using subset evaluation in CI pipelines to control costs. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Building and Deploying AI Agents in Production: Lessons I Learned](https://daily.dev/posts/building-and-deploying-ai-agents-in-production-lessons-i-learned-fmxhggdbm) · Medium · 1 upvotes · 0 comments - [How enterprise leaders are scaling AI agents across their organization](https://daily.dev/posts/how-enterprise-leaders-are-scaling-ai-agents-across-their-organization-gaahtnpqz) · databricks · 0 upvotes · 0 comments - [Building Responsible and Calibrated AI Agents with Databricks and MLflow: A Real-World Use Case Deep Dive](https://daily.dev/posts/building-responsible-and-calibrated-ai-agents-with-databricks-and-mlflow-a-real-world-use-case-deep-yfco81kv2) · databricks · 1 upvotes · 0 comments --- Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#observability](https://daily.dev/tags/observability), [#databricks](https://daily.dev/tags/databricks) [View this post on daily.dev](https://daily.dev/posts/the-production-ai-playbook-deploying-agents-at-enterprise-scale-sandipan-bhaumik-databricks-zt6hfyrll) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks","url":"https://daily.dev/posts/the-production-ai-playbook-deploying-agents-at-enterprise-scale-sandipan-bhaumik-databricks-zt6hfyrll","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/the-production-ai-playbook-deploying-agents-at-enterprise-scale-sandipan-bhaumik-databricks-zt6hfyrll"},"datePublished":"2026-06-18T13:21:24.019Z","dateModified":"2026-06-18T14:19:59.594Z","description":"A framework for deploying AI agents in enterprise production environments, built from real-world lessons across regulated industries. 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