The AI That Killed It

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Python is the language of AI, incorrect, inside Microsoft stack, AI writes the glue, not you.

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You keep shoving Python into power automate and power BI, then wonder why your flows wobble

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like a three-legged chair.

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Here’s the fix.

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I’ll show you why type agent style orchestration and co-pilot turn TypeScript-like scripts

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into the first class glue, push Python to contain analytics and collapse build, debug,

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deploy into a single conversational loop.

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Stay for the secret step that kills defect inflation, the mistake that silently drains

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budgets and the hybrid pattern that lets you keep Python without the pain.

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The problem, Python’s friction inside power platform.

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Let’s start with the obvious that somehow isn’t obvious.

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Power automate does not run arbitrary Python.

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Office scripts, TypeScript flavoured are the native script surface for Microsoft 365,

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Excel online host them.

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That’s the lane.

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When you insist on Python, you bolt on Azure Functions logic apps or custom connectors.

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Now you’ve added external compute deployment pipelines, authentication and my favorite run-time

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mystery meet.

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This hurts as simple economics plus probability.

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External setup means more services to configure, more credentials to rotate and more costs

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ticking even while you sleep.

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Dynamic typing adds roulette.

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The code compiles because there is no compile, then fails mid-flight because the field wasn’t

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what you assumed.

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In a flow, that’s not cute.

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It’s a broken approval, a missed SLA, and another ticket titled urgent typed in lower

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case.

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Specific scenarios, Power BI data flows.

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You’re transforming data.

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Power pilot can now generate Power Query M or Python for you, inside of the data flow

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Gen 2.

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Great, until you mix this with a separate Python service for glue tasks that Power Automate

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could have handled natively, now you’re lineage crosses products.

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Observability, enjoy tracing an error from a flow to a function to a data flow.

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In the time it takes your stakeholders to ask, is it done yet for the seventh time?

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Power Automate flows.

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The whole point is native connectors and Office scripts.

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People still shove Python behind a custom connector to rename columns in Excel.

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Just like calling a tow truck to back out of your driveway.

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Every hop increases Britleness.

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Connector schema is drift, function dependencies age, permissions sprawl across entra

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rolls, resource groups, and mystery storage accounts.

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No one will admit to creating.

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Fabric notebooks, yes, Python belongs here for data science ML series compute.

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But when you use notebooks as orchestration glue, you weld business logic to a compute

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kernel.

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Scheduling, validation, and IO become bespoke.

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An environment update later pandas goes up a minor version and your simple glue becomes

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archaeological strata.

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Beginners think they’re simplifying.

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They’re actually creating a single point of procedural failure with bonus yaml.

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The thing most people miss office scripts aren’t toy typescript.

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They are the sanctioned edge for automating Excel and related Microsoft 365 tasks without

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dragging in infra.

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Compare that to full node typescript which you might use in Azure functions for robust

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APIs when you actually need them.

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Python inside Power Platform is indirect by design.

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You can do it.

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You shouldn’t do it for glue.

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You can talk cost math without the fairy dust.

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Simple flows are cheap because they stay in platform.

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Connectors, triggers, and office scripts ride your existing licensing.

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Bring in Python, via functions, and you pay in two currencies.

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Money and attention.

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Money for execution, storage, and networking.

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Attention for versioning, image hardening, secrets, retreats, cold starts, and the weekly,

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why did it fail at 2.14 a.m.

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Saga.

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At small scale find.

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At organizational scale, that’s a budget line item with regret baked in.

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Common failure modes show up on repeat.

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Visual connectors where your swagger spec lags reality by one field.

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Version drift.

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Library updates that aren’t pinned because someone loves latest.

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Permissions sprawl.

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Service principles with rights just for now.

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That become forever.

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Cross service debugging.

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Replaying runs across power automate, Azure functions, and data flow logs like a true

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crime podcast.

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The truth?

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Dynamic typing’s freedom is fun until it’s 3 p.m. on quarter close and your flow succeeds

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with the wrong shape.

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Strong typed boundaries catch stupidity before it ships.

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Type-script like scripts and schema connectors give you those guardrails Python can have

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types too.

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Yes, most of you don’t enforce them in flows.

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Don’t argue your log files already did.

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And now the transition you were waiting for.

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If friction stack every time your hand stitch code as glue what replaces the glue.

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Enter AI that writes and runs the glue for you within the lanes the platform optimizes.

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Copilot can translate natural language into office scripts for Microsoft 365 tasks.

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In Power BI, copilot uses semantic model metadata to generate M or Python accurately and produce

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report scaffolds in seconds.

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Agents like type agent coordinate tools with memory and guardrails reducing the ping pong

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of spec code deploy debug redeploy.

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You constrain the surface area you get typed edges where it matters and you keep Python where

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it shines.

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Contained analytics not duct tape.

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Yes, you can cling to but Python can do anything.

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So can duct tape that doesn’t make it the right fastener for aircraft.

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Microsoft orchestration native let AI write the glue put Python in notebooks or services

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designed for it.

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You’ll spend less break less and this is the part that stings ship more.

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Why current approaches fail manual code and static playbooks.

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The manual coding loop looks heroic on a whiteboard requirement code deploy test fix redeploy.

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In reality, it’s slow motion whack a mole business rules change mid sprint.

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Your final schema shifts by lunch and the only constant is another redeploy in the

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power platform that loop is even more painful because every hop power automate to Azure

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functions to power BI amplifies the friction and multiplies your failure modes.

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Static playbooks are the other trap.

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You wrote a golden path runbook for quarter and logic then sales events in new discount

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finance tweaks revenue recognition and legal ads of four step approval yesterday.

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Your Python in functions glue doesn’t bend it cracks your update code bump dependencies

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rebuild containers and rediscover that cold starts love to appear during executive demos

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fascinating right.

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The truth most people avoid dynamic typing freedom feels fast when you’re alone at scale

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inflows its bug roulette an optional field changes from string to number and your function

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happily sales through until a branch expects text and throws an exception at runtime.

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That’s not a unit test failing loudly that’s production silently misclassifying transactions

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and yes your audit trail is basically a novella observability is a scavenger hunt errors

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originate in power automate get transformed inside a custom connector trigger a Python

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function bounce to data flow gen 2 and finally surface as a broken tile in power BI.

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Now you’re reading five logs that can’t agree on the same timestamp format you call this

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investigation users call it we still don’t have the numbers micro story a team shaved hours

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of monthly reporting by consolidating transforms into a single Python function it worked until

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dependency conflicts resurfaced a minor pandas version update changed the default their

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function silently coerced nulls and two weeks later variance reports were off by just enough

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to be dangerous they fixed it then it occurred because pinning was on the backlog bug recurrence

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isn’t a mystery it’s a process floor and governance custom connectors age like milk open API specs

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drift behind reality tokens expire service principles collect excessive rights temporarily

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and your once elegant pipeline is now a rubegoldberg machine with s.o.c findings the manual loop

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doesn’t just cost time it accumulates operational debt every quick fix is a future outage with a

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calendar invite the center cannot hold because the center is brittle glue manual code works when the

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domain is stable the power platforms business logic is not stable therefore pay attention your

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orchestration must be adaptive type that the edges and generated close to the platforms primitives

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not hand stitched far away stop treating glue like an app let an agent orchestrate and constrain

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the better method type agent plus copilot as the orchestrator enter the model that doesn’t fight

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the platform agents generate call tools observe results and revise not free for all code spew tool

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calling with guardrails you give the agent a bounded toolbox office scripts for Microsoft 365

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actions power bi data flow gen two for transformations connectors for data movement and strictly

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typed interfaces between them the agent holds context reasons across steps and only rights code

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where code belongs start with office scripts and copilot you describe the outcome when a new

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row lands in this table normalize dates fill blanks and email approvers with a summary copilot

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translates that into type script like office scripts and flow steps there’s no external runtime

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no container to harden no secret store to babysit the code sits where the data lives excel online

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sharepoint one drive and your flow stitches native connectors fewer moving parts fewer ways to fail

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the thing most people miss is that typed edges exist even here object models

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method signatures and connector schemas and force shape before runtime turns on the blender move

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to data flow gentle with copilot prompts become power query m or python but with context copilot is

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semantic model aware it knows your tables columns measures and synonyms so generation is anchored to

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your reality not a hallucinated schema need a quick report scaffold copilot can spin a page with

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visuals in seconds not because magic because it leverages metadata you already curated you validate

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the diffs lock the pattern and this is important keep orchestration out of python use python inside

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the data flow for analytics kernels not for renaming columns or pinging approvals now bring in type

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agent pie or a similar agent framework for multi step reasoning the agent isn’t replacing your

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governance it’s enforcing it it remembers prior failures chooses the right tool and retrieves

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intelligently tool calling accuracy and context retention matter here when the agent can consistently

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pick update worksheet versus send email you stop shipping human wiring errors time to resolution

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shrinks because the agent handles the lead up generating the script testing against the sample

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validating outputs and only then promoting changes why this works is simple architecture

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constrained the code surface put statically typed boundaries at the seams open api schemas on

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connectors office script object models semantic models in power bi inside those boundaries let AI

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generate code to spec the reason this reduces defects is not supernatural it’s pre validation

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you’re catching shape mistakes before they become outages and because the agent keeps memory

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you kill recurrence the bug that returns because your fix didn’t update the pattern just the file

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practical shift python remains where it dominates within fabric notebooks or data flows for

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advanced analytics modeling and ml you let co pilot help with exploratory data analysis vectorization

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patterns and visualization code around that the agent handles the perimeter scheduling validation

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i.o. and policy agents trigger notebooks validate outputs and root results without inventing bespoke

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orchestration in python you do not weld business logic to a kernel you separate concerns like an adult

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office scripts and co pilot give the quick wins the agent enforces repeatability here’s the shortcut

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nobody teaches codify prompts as assets treat them like templates with variables and acceptance

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criteria if you remember nothing else capture the successful prompt and the expected shape of outputs

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the agent can reuse it adapt it and link the result against your contracts that’s how you collapse

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build debug deploy into a conversational loop that’s still governed the game changer nobody talks

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about typed contracts for agent tools you define the input and output schemas the agent can validate

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before it runs a single step in production compared that to just run the python and pray your

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json didn’t sprout an extra property and yes python can be typed the point is discipline at the

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edges not language loyalty in the power platform the edges are Microsoft’s domains office power bi

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data verse so use the primitives they optimize once you nail that everything else clicks

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versioning is smaller surface area prompts and scripts not monoliths observability is centralized

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power automate run history data flow lineage and agent traces not five disconnected lock dashboards

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cost goes down because you retire as your functions that were just doing make work chores

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and defect rates drop because your orchestrator is opinionated not artisanal now you might be thinking

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this sounds like giving up control incorrect you’re moving control upper level you decide the contracts

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the tools the guardrails and the review gates the agent does the stitching faster than your human loop

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and less error prone you reserve python for high value analytics where libraries earn their keep

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that’s not surrender that strategy let me show you exactly how this feels in practice a request lands

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we need a weekly report that cleans csv’s enriches with data verse pushes to a semantic model and emails

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approvers old way stand up a python function wire a custom connector argue with oath fix types three

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times new way agent calls data flow gen 2 via co-pilot to generate the transforms validates columns

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against the model uses office scripts to prep the excel drop triggers a flow for notifications

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and logs the lineage no bespoke glue no midnight redeploy the reason this works is you stopped using

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python as duct tape in a platform designed with its own fasteners you let a i write glue near the

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joints not across the gap and when the business changes spoiler alert it will you update a prompt

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not a container image that’s how you keep shipping while everyone else is still in dependency hell

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explaining to finance why latest was a terrible idea application one power bi data flows python

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generated not hand written here’s where the light bulb goes on in power bi you’re not paid to write

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artisanal m or bespoke python you’re paid to deliver clean data and working reports co-pilot inside

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data flow gen 2 let’s you state the transformation in plain english then it generates power query m or

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yes python anchored to your actual semantic model not fantasy tables your tables columns and measures

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that semantic awareness kills a whole category of column not found chaos before it starts

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why this matters generation plus context equals fewer dumb bugs the thing most people miss is the review

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loop you don’t trust the black box you use it to draft fast then you review the diff lock the pattern

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once it’s right from there use co-pilot to iterate by prompt not by endless hand edits your treating

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code like a template with variables rather than a diary of your keyboards feelings what does the

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workflow look like you prompt from sales transactions filter to the last 12 months normalize date formats

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left join customer master and compute gross margin co-pilot proposes m or python you preview the result

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against a sample validate column names against the semantic model and pin the step need python for

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an analytics kernel say outlier detection or seasonal decomposition fine let co-pilot generate

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that inside the data flow but keep orchestration out of it python computes the platform orchestrates

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quick win reports got folding co-pilot can spit out a first pass page with visuals in seconds

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it’s not magic it’s metadata you already named your measures added synonyms and curated

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relationships co-pilot leverages that to place charts intelligently you save the groundwork and

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spend time refining what matters business logic and presentation common mistakes that inflate defects

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first letting co-pilot’s generated python grow tentacles if it starts renaming columns calling

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external endpoints or embedding workflow logic you’ve slipped back into brittle glue second skipping

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code reviews because a i wrote it no ad review standardized prompts keeper checklist column shapes

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validated null handling explicit joins deterministic third mixing orchestration and analytics if you

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blend approval logic into a python transform you’ve guaranteed the next schema change breaks your

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workflow the hybrid rule here is simple python for analytics kernels m or platform native steps

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for plumbing use data verse or one lake for inputs and outputs that the rest of the platform

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understands when you need to adjust the transform change the prompt and validate the diff rather than

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spelunking through a 400 line function business changes become edits to intent not surgery on glue

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and yes observability improves data flow lineage shows what fed what co-pilot’s proposed steps form

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a readable chain rather than a bucket of hand rolled scripts when something fails you troubleshoot

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in one place compare that to your old pattern power automate triggers a custom connector hits a

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python api that mutates columns then writes to a lake you forgot to tag delightful the payoff is

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speed with fewer surprises you generate review lock you reserve python for the heavy math where it

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earns its keep and when the cfo invents a new metric on a Tuesday afternoon you update a prompt and

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revalidate not redeploy a container image try pretending that isn’t better data flows stabilized

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good now kill the fragile glue living rent free in power automate application two power automate

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replace python glue with office scripts plus agents in power automate the correct move is boring

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on purpose native connectors and office scripts you describe your steps co-pilot drafts the flow

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in scripts and you keep everything inside Microsoft 365 no Azure functions no custom connector

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scaffolding no midnight patching of ssl ciphers on a container you forgot existed shocking revelation

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when you reduce moving parts you reduce failures why office scripts because they’re type script

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flavoured with a defined object model that gives you typed edges method signatures predictable shapes

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before runtime gets a chance to embarrass you co-pilot turns for each new row in this table normalize

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date strings computer status and email the owner with a summary into exactly that a flow plus a script

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your data stays near excel share point or one drive your governance stays simpler and your

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defect rate drops because you eliminated the ad hoc python detour that loved to break on Tuesdays

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how to structure it so it scales adopt an agent driven pattern the agent type agent or comparable

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selects tools from a constrained toolbox invoke office script call a graph connector write to

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data verse send an approval inputs and outputs are schemered the agent validates preconditions runs

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a test against the sample and only then promotes the change tool calling accuracy matters so name

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tools unambiguously worksheet update table beats do stuff when the agent consistently picks the

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right tool you stop wiring the wrong action to the right trigger quick wins that retire python glue

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excel operations cleaning columns the duping splitting fields office scripts handle these natively

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approvals and notifications native actions in minutes zero custom auth data verse and sharepoint

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updates connectors with metadata awareness not raw HTTP calls costs go down because you’re not paying

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for always on compute to rename columns time to resolution shrinks because your agent can generate

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test and iterate without a human setting up a dev container and your security team stops glaring

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because you removed the unknown API endpoint that someone labeled temporary mistakes to avoid

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because of course you’ll try them first forcing python via a custom connector for trivial tasks

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if the job is to reshape a table use office scripts second ignoring governance scripts need

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versioning review gates and naming standards treat them like code because they are third bearing

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business logic across five flows with no type contracts define your input and output schemas

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upfront the agent can enforce them the platform will validate them production will thank you you

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still want python put it where it belongs analysis if a flow needs predictions or advanced transforms

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call a proper service fabric notebook job ml endpoint behind a clean API not an improvised flask

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app someone left on a free tier the flow orchestrates the python computes they interact through a contract

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not through vibes the subtle advantage of office scripts plus agents is maintainability prompts become

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assets scripts become reusable tools flows become thin orchestration layers not logic museums when

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requirements change you update a prompt and if needed a script with a review rather than revisiting

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an entire custom connector stack the agent remembers the previous fix reuses the pattern and prevents

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recurrence that’s how you turn we spent the afternoon debugging types into we shipped before lunch

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the truth in power automate python is glue is performative difficulty you’re proving you can

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while the platform quietly offers you a simpler safer path use it application three fabric notebooks contain

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python automate the perimeter in fabric python finally sits where it’s strongest the compute core

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not the doorbell not the ductwork the engine you keep the business logic at the edges and let

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the notebook do analytics modeling and heavy transforms the perimeter scheduling validation i.o policy

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belongs to agents and typescript flavor tooling why this matters when you weld orchestration to a

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notebook every environment tweak becomes a production incident separate concerns the agent calls

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the notebook through a clean job API passes typed inputs and expects typed outputs if the shape

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deviates it fails fast at the edge not halfway through a 20 minute run how this looks in practice

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co-pilot helps inside the notebook for eda vectorization visualization scaffolds you still review

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and pin library versions adults remember the agent handles pipelines triggers on data arrival runs

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a schema check launches the notebook job and validates outputs against a contract before publishing

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to one lake or a semantic model version control notebooks like code store prompts that generated

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helper functions unit test outputs metrics distributions row counts rather than line by line

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plumbing quick win prompt co-pilot to generate a seasonal decomposition step keep it in the notebook

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and let the agent orchestrate retries and alerts business logic lives in declarative contracts

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python stays computational mistakes to avoid hiding approvals or renames inside the notebook

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coupling flows to ad hoc endpoints or storing the only definition of a kpi inside a cell comment

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don’t do archaeology do architecture now quantify it so finance stops sighing results time-saved

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cost-reduced defects down time first a i assisted generation collapses the build debug deploy loop

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co-pilot drafts transforms and scripts the agent tests against samples before promotion

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you cut rework and context switching because orchestration stays native and code sits where it

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executes cost next retire as your functions that were renaming columns keep flows in platform

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notebooks in fabric and glue in office scripts you pay less in compute and far less in attention

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no container patching fewer secrets fewer weekend outages defects finally typed boundaries

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on connectors office script object models and semantic metadata catch shape errors early agent

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guardrails tool calling accuracy context retention kill the fix it Friday break it Monday cycle

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recurrence drops because you update patterns not just files practical benchmarks to track

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time to resolution per incident tool call success rate and change lead time implementation checklist

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pick your orchestrator define contracts templatize prompts add review gates and monitor outcomes

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centrally python isn’t dead it’s demoted to its specialty good that’s how you ship it counter-argument

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and rebuttal but python dominates data science yes python dominates data science libraries community

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notebooks the works in fabric that’s exactly why you keep it inside notebooks and analytics kernels

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it’s the engine the truth engines don’t root traffic lights orchestration is roads signals and rules

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that’s power platforms domain connectors office scripts data flow gen 2 and co-pilot

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here’s what most people blur dominance in analytics doesn’t equal fitness for glue inside

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Microsoft stack in power automate python is indirect external compute via azure functions or custom

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connectors that adds cost secrets cold starts and version drift meanwhile office scripts run where

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your Microsoft 365 data lives and co-pilot drafts them from plain English you’re choosing friction

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versus native speed but we need custom logic good put it behind typed boundaries use a fabric

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notebook job or ml endpoint for predictions and heavy transforms expose a clean API let the flow

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orchestrate with contracts not vibes you get python’s strengths without hand wiring every approval

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rename and file hop through a flask app you’ll forget to patch Microsoft’s trajectory reinforces this

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split office scripts are the sanctioned scripting edge for m365 co-pilot is semantic model aware in

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power bi and data flow gen 2 generating m or python anchored to your metadata agents improve tool

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calling accuracy and context retention so the glue is reliable and auditable none of that requires you

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to embed orchestration in python hybrid is not a compromise it’s the design type script flavoured

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scripts and agents handle the parameter python handles compute they meet at typed APIs or cues

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you reduce time to resolution shrink run costs and cut defect recurrence because the seams I

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explicit use python where it shines stop forcing it to be duct tape implementation playbook your

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next 30 days week one audit tag every workflow and data flow as glue or analytics highlight python

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in functions that touch approvals renames or file IO anti patterns document inputs outputs and

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failure hotspots pull real metrics incident time to resolution change lead time and tool call success

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rate if you have agents if you don’t find establish the baseline now week two migrate trivial glue

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replace python endpoints used for excel or sharepoint chores with office scripts plus native connectors

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use co pilot to draft scripts from plain English then add a lightweight review checklist column shapes

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null handling identity standardized prompt templates and name scripts like adults verb noun with

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scope not script final two version them in your repo yes scripts are code week three refactor data flows

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move handwritten transforms to co pilot assisted generation in data flow gen 2 keep orchestration out

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of python reserved for analytics kernels only validate against your semantic model column names data

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types measures capture the diff you accept and added to your prompt library so the next change is a

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prompt edit not a spelunking expedition week four introduce an agent for complex orchestration

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constrain the toolbox worksheet update table dataverse dot absurd data flow dot run email dot

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send define type contracts for each tool including sample payloads and acceptance criteria turn on

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pre run validation against samples track tool calling accuracy and time to resolution when the agent

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fails fix the pattern update prompts schemas or tool names so recurrence drops governance throughout

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and forced typed interfaces at every seam store prompts alongside code add review gates for scripts

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and data flow changes centralized telemetry power automate run history data flow lineage and agent

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traces in one dashboard rollback plans are non-negotiable previous script versions last known good

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data flow and notebook job snapshots exit criteria at day 30 fewer custom connectors for trivial glue

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measurable drop in run cost faster changes with smaller blast radius and documented interfaces

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that let a new t-made chip in a day not a week python remains inside notebooks and analytics kernels

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the glue it’s generated typed and boring that’s the point key takeaway in Microsoft’s ecosystem agents

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plus type script flavored scripts orchestrate cleanly while python stays contained in analytics where

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libraries actually earn their keep if this saved you time repay the debt subscribe watch the follow

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up on building your first type agent playbook with typed two contracts and review gates your ship

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faster with fewer outages and finance will finally stop sighing at your invoices proceed