So much of dev work happens in the context of a larger team, and now you can bring all of that work context into the GitHub Copilot CLI with Work IQ. Try it out: https://lnkd.in/gGPd-FWr
Transcript
Much of t…
So much of dev work happens in the context of a larger team, and now you can bring all of that work context into the GitHub Copilot CLI with Work IQ. Try it out: https://lnkd.in/gGPd-FWr
Transcript
Much of the context that AI agents need doesn’t actually live in your application. It’s actually in documents and emails and meetings and SharePoint your company data. Work IQ lets you bring the full context of your organization’s data to any agent, like the Copilot CLI and Agentic AI assistant. Write in your terminal, and all you need to do is add a single MCP server. Once it’s added, the agent can easily pull in any organizational data. All you have to do is ask. This is the missing piece of context for most applications, and it unlocks powerful workflows that you can use today. Here’s a spec that was written in Word and it lives in SharePoint. I don’t have to summarize this by hand, I just give Copilot the title of the document and a link to the repo and ask it to compare the spec to the implementation. Copilot. Finds the right spec through Work IQ, inspects the code and compares the two. It shows which requirements were implemented, which ones drifted, and what’s missing. That’s not just a search result, that’s delegated execution. Now let’s look at a harder problem. We had several meetings about a File Explorer version control feature, and nobody wrote down the architecture. So I asked Copilot to find the implementation plan. Draw it, Copilot reasons over meeting transcripts and emails. From work IQ and reconstructs the architecture and summarizes the decisions. These decisions were never formally captured, but they still exist under the hood. Copilot explores the problem space, plans the work, and reviews the result as it goes. It can select the right model per step without you having to wire up specific routing logic. You can also pick your model with a simple slash command. All of this without ever leaving the agent. Build real intelligence into your agent today with work IQ.
"Great insights, Satya Nadella While AI is transforming biology, at NYUROX, we are pushing the boundaries by using Computational Physics to predict neuro-metabolic shifts. 🧠 We’ve built an engine that detects Alzheimer’s and neuro-decay 18 months earlier than traditional structural imaging (MRI). Our goal is to shift healthcare from ‘reactive’ to ‘predictive’ by mapping physics biomarkers. Building this from India, for the world. Would love to see how Azure’s high-performance computing can further scale our physics-driven diagnostic engine! 🇮🇳🚀 #DeepTech #HealthInnovation #NYUROX"
The real leap forward lies not in "stronger models," but in reconnecting the work context that is scattered across emails, meetings, and code. Context is productivity.
The spec-to-implementation comparison is the enterprise unlock here. Most orgs have requirements in SharePoint, decisions in meeting transcripts, code in repos - all disconnected. The drift between "what we decided" and "what got built" compounds invisibly until something breaks. Surfacing that at the CLI - where developers actually live - beats dashboard reporting that nobody checks. The SDK making this embeddable is smart. Context shouldn’t be a destination you visit. It should follow you.
Satya Nadella This is a timely and insightful advancement Satya Nadella Bridging contextual information..—requirements, decisions, discussions, documentation..directly into the developer workflow is a fundamental step toward truly integrated, high-velocity engineering. Tools that enable developers to access organizational knowledge without breaking flow will materially reduce cognitive overhead, improve alignment between intent and implementation, and accelerate delivery of reliable software at scale. The ability to surface context from disparate sources in-line with coding activity represents a significant productivity inflection point for teams aiming to build with clarity and autonomy. Microsoft’s commitment to elevating real work context through AI innovation continues to set industry direction.
The context problem is one of the most underappreciated challenges in enterprise AI adoption. Teams have been building powerful AI tools, but without understanding the organizational context - the why behind decisions, the history of choices, the team dynamics - they often miss the mark. Work IQ bringing that context into the development workflow is a smart move. We see similar patterns in government technology implementations - the tools that succeed are the ones that understand the institutional context, not just the technical requirements.
Context is where most productivity is won or lost. Bringing shared team knowledge into the workflow is far more valuable than isolated “smart” tools. ————————————————————- At CEO Power Tank, leaders recharge their power, not just their performance. Fill the inner tank. Lead with real edge. Visit: www.ceopowertank.com
Absolutely agree. 👏 This is a big shift—from coding in isolation to coding with shared intelligence. Bringing team context into the GitHub Copilot CLI via Work IQ means developers aren’t just writing code faster—they’re writing better-aligned, decision-aware code. The CLI now understands why something exists, not just what to type. This is how AI truly augments developers: Context > autocomplete Collective knowledge > individual memory Flow > friction Great to see this vision reinforced by Satya Nadella AI as a teammate, not just a tool. The future of dev work is collaborative, contextual, and deeply intelligent. 🚀
Just playing with this for a few hours I am blown away. This is changing the game for how we design, build, and what our customers will be able to do!
Bringing team context into the CLI feels like a natural next step. It’s usually the missing piece when dev tools try to be truly helpful.
Much needed update!! – Real intelligence is developed through greater exposure to nuanced knowledge and contextual understanding. By this we can extract full contextual information, enabling deeper analysis and more thorough research before generating the final output. 👏
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