You’ve heard it for years: “Python is the language of AI.” But inside Microsoft’s ecosystem—Power Automate, Power BI, Fabric, Microsoft 365—Python isn't the hero. It’s the friction layer. Organizations keep bolting Python onto Power Platform through Azure Functions, custom connectors, and brittle services… and then wonder why everything breaks. In this episode, we dismantle the myth that Python is the best “glue” for Microsoft workflows. You’ll learn why TypeScript-like Office Scripts + Copilot + TypeAgent-style orchestration outperform Python for automation and operational logic. We’ll explore why Python thrives in analytics—but fails in orchestration—and how AI is making “glue code” not just easier, but obsolete.If you want faster automations, fewer defects, lower cloud bills, and simpler governance, this episode will feel like oxygen. What You’ll Learn in This Episode 1. The Core Argument: Python Isn’t Dead — It’s Just Been Demoted Python remains a powerhouse for:Data scienceMachine learningAnalytics notebooksModelingTransform-heavy computeBut for workflow glue, automation, Power Platform orchestration, and Microsoft 365 automation, Python is now the wrong tool.Why? Because the platform already gives you:Office Scripts (TypeScript-flavored)Native connectorsCopilot-driven code generationDataflow Gen2 M/Python auto-generationTypeAgent-style AI orchestrationSemantic model awarenessTyped boundaries that prevent bugsPython can do anything—but that doesn’t mean it should do everything. 2. Why Python Causes Friction Inside Power Platform Python inside Power Automate or Power BI introduces expensive complexity: • External compute Azure Functions, Logic Apps, containers, custom connectors. • Authentication overhead Certificates, managed identities, expiring tokens, secret rotation. • Runtime unpredictability Cold starts, dependency drift, library mismatches. • Dynamic typing chaos Schema changes become runtime errors, not compile-time warnings. • Debugging pain Errors propagate across Power Automate → custom connector → Azure Function → Dataflow: “Why did this fail at 2:14 a.m.?”“No one knows.” • Cost sprawl You pay for compute, storage, bandwidth, logging, monitoring and upkeep. Outcome:Workflows become fragile Rube Goldberg machines held together with duct tape and optimism. 3. Real Scenarios Where Python Makes Everything Worse We walk listeners through painful but familiar examples: Power Automate + Python Custom connectors calling Python for tasks that Office Scripts could do instantly (column renames, date normalization, Excel transforms). Power BI + Python Inconsistent schemas between Python transforms, Dataflow M code, and semantic models. Fabric notebook overreach Using notebooks for orchestration instead of analytics—creating a single point of failure. Cross-product lineage breakage Flow → Function → Notebook → Dataflow → Report…Five logs. Five timestamps. Zero joy. Permission sprawl Service principals that “temporarily” get read/write permissions… forever. Version drift Dependency updates silently break workflows, especially in pandas-heavy pipelines. Python works—but every dependency and environment update is a landmine. 4. The Better Method: Let AI Generate the Glue Here’s where the episode turns hopeful. Copilot + Office Scripts replace Python glue Copilot can write TypeScript-like Office Scripts directly inside Microsoft 365 apps. These scripts:Run where your data livesAre typed and predictableNeed no external servicesAre cheapAre governed within Microsoft 365Don’t require CI/CD pipelinesRemove service sprawl entirelyDataflow Gen2: AI generates M or Python for you Copilot knows your:TablesSchemasMeasuresSynonymsSo its generated code is anchored to your reality—not hallucinated structures. TypeAgent-style orchestration coordinates everything Agents handle:Tool callingMemoryContext retentionValidationRetry logicGuardrailsDeterministic sequencingNo more rewriting glue code when a business rule changes.You update a prompt, not a pipeline. Result? Build → debug → deploy collapses into a single conversational loop. 5. The Hybrid Pattern: Keep Python, But Contain It We outline a new architecture: Use Python where it shinesPrediction modelsAnalytics kernelsFeature engineeringStatistical transformsData science experimentsComplex ML logicUse AI + TypeScript-like code where orchestration belongsPower AutomatePower AppsExcel/SharePoint automationPower BI transformationsMicrosoft 365 tasksWorkflow triggersApprovalsNotificationsData enrichmentIntegration glueUse agents forValidationTool callingOrchestrationError handlingGuardrailsPython becomes the engine.AI + TypeScript becomes the road system.Agents become the air-traffic controllers. 6. Deep Dive Into Key Platforms Power BI DataflowsUse Copilot to generate M or PythonValidate against semantic modelsLock the patternUse Python only for analytics kernelsKeep orchestration outside notebooksPower AutomateDitch Python-based custom connectorsUse Office Scripts for Excel operationsLet Copilot draft scriptsUse agents for multi-step flowsReduce cost, reduce failures, reduce auth problemsFabric NotebooksKeep Python inside analytics boundariesLet agents orchestrate from outsideValidate notebook outputs before publishingVersion notebooks like real softwareDon’t bury business logic in cells7. Quantifiable Results Organizations that adopt this pattern see: ⏱ 40–70% reduction in build time Because prompts + typed boundaries beat hand-written glue. 💵 20–80% cost reduction From retiring unnecessary Azure Functions and custom connectors. 🐛 60–90% defect reduction Typed interfaces + agent validation kill schema drift bugs. 🧠 3–10× faster iteration Changes become a prompt update—not a redeploy. 🧹 Huge governance simplification No more Python endpoints with unclear owners and forgotten secrets.Become a supporter of this podcast: https://www.spreaker.com/podcast/m365-show-podcast--6704921/support.Follow us on:LInkedInSubstack

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