We are in the middle of rapid shift – AI agents are no longer just reactive helpers waiting for a us to give them a prompt. Instead, they are becoming proactive, and autonomous , capable of initiating actions, orchestrating workflows, and making decisions across systems.
If you’ve already built governance models for low‑code platforms like Microsoft Power Platform, you’re not starting from zero. Those same principles – with a few smart extensions can help you govern the next generation of agents built in Copilot Studio.
What is Agent Governance? Agent governance encompasses the rules, policies, and oversight mechanisms that guide the behavior of AI agents – autonomous systems capable of performing tasks with minimal human intervention. This governance is crucial to ensure that these agents operate in a manner that is legally compliant, ethically responsible, and operationally safe!
Microsoft have shared new blue prints and guidance to help you get started with healthy goverance for Copilot Studio – which I have linked to and summarised below…
1. Lead with a Governance Mindset
Agents aren’t “just another app.” They’re digital labour – they (can) talk across systems and across roles and need managing just like humans. This means they they need:
Trackable identities — so you know exactly which agent did what, and when.
Scoped permissions — the principle of least privilege applies here too.
Continuous oversight — because autonomy without accountability is a risk.
Not every agent should have the same freedom. For example, a Q&A bot answering FAQs is low risk. An autonomous sales development agent drafting proposals is much higher stakes and an agent that takes a customer interaction and acts on it automonously is high risk.
We must define tiers of autonomy and enforce them with technical guardrails.
2. Apply Your Low‑Code Lessons
If you’ve governed Power Platform, you already have your own playbook:
Managed environments to separate dev, test, and production.
Role‑based access control (RBAC) to manage who can create, deploy, and run agents.
Data Loss Prevention (DLP) policies to control what data agents can access or share.
Audit logs to track behaviour and support compliance.
These aren’t “nice to haves” — they’re essential for safe, scalable agent adoption. Extend your existing frameworks to cover new agent behaviours.
3. Drive Visibility, Cost Control, and Business Value
Governance isn’t just about control — it’s about clarity. Visibility and telemetry is really important becuase it tells us:
Who created the agent.
What data it touches.
How often it’s used.
The business outcomes it’s driving.
With that visibility, you can spot redundantagents, forecast costs, and focus investment where it delivers the most value. Tools like Copilot Studio analytics and Power Platform Admin Center make this possible — but only if you use them consistently.
4. Empower Innovation with Guardrails
The people closest to the work often have the best ideas for agents. Advice is to empower them to experiment — but within a zoned governance model:
Zone 1: Personal Productivity — safe sandboxes for individual experimentation.
Zone 2: Collaboration — team‑level development with stronger controls.
Zone 3: Enterprise Managed — production‑grade agents with full monitoring and lifecycle management.
This approach balances speed and safety, enabling innovation without compromising compliance.
5. Build Community, Training, and Experimentation into the Culture
Governance is as much cultural as it is technical and it’s the culteral and human aspects that typically impact and slow adoption.
A thriving Center of Excellence (CoE) should:
Host “Agent Show‑and‑Tell” sessions and hackathons.
Appoint champions in each department to mentor others.
Provide role‑based training for makers, admins, and business leaders.
Encourage responsible experimentation — and celebrate successes.
As with any transformational shift, when people feel supported and inspired and part of the journey, adoption accelerates and impact flourishes.
Why This Matters Now
According to Microsoft, over 230,000 organisations – including 90% of the Fortune 500, are already using Copilot Studio, and IDC projects there will be a staggering 1.3 billion AI agents by 2028.
This scale and exponential speed of adoption make governance a critical priority, not an afterthought or option!
The CIO’s role is shifting from enabling agents to governing them at scale — ensuring they’re secure, compliant, cost‑effective, and aligned with business goals. That’s not just a technical challenge; it’s a leadership opportunity.
Summary – the Key Steps
Extend your low‑code governance — apply your Power Platform controls to agents.
Define autonomy tiers — match oversight to risk.
Instrument for visibility — track usage, cost, and impact.
Microsoft 365 Copilot now includes two advanced AI agents – Researcher and Analyst that became generally available in this month ( June 2025). These agents use powerful reasoning models (based on OpenAI’s o3-mini and deep research models) to handle complex tasks beyond what the standard Copilot could do.
Researcher is a specialised agent for multi-stepresearch – it can securely comb through your work data (emails, files, meetings, etc.) and the web to gather information, ask clarifying questions, and produce well-structured summaries and insights. It’s ideal for tasks like market research, competitor analysis, or preparing for big meetings – work that used to take hours, now done in minutes with higher accuracy.
Analyst is a virtual data analyst/data scientist built into Copilot. It excels at advanced dataanalysis, working through messy spreadsheets or databases step-by-step using chain-of-thought reasoning and even running Python code when needed. From identifying sales trends to spotting anomalies in finance data, Analyst gives you in-depth answers and visuals that mirror human analytical thinking.
Compared to the standard Microsoft 365 Copilot, these agents go much further in reasoning and capabilities for these specific tasks. While the native Copilot mod helps draft documents or summarise content, Researcher and Analyst tackle complex reasoning tasks (deep research and data analysis) with a level of thoroughness and skill akin to an expert – essentially “like having a dedicated employee at your side ready to go, 24‑7,” according to Microsoft’s Jared Spataro. They are accessed through the Copilot interface (pinned in the Copilot app and via Copilot Chat) and come with a usage limit of 25 queries permonth per user due to their intensive workloads.
Analyst vs. Copilot for Finance:
Analyst is a general-purpose data analysis agent available to any Copilot user, whereas Microsoft 365 Copilot for Finance is a separate, role-based Copilot designed specifically for finance teams. Copilot for Finance connects to financial systems (like Dynamics 365 and SAP) and Microsoft 365 apps (Excel, Outlook) to automate finance workflows (reports, reconciliations, insights). Unlike the Analyst agent which works on data you provide, Copilot for Finance directly taps into live enterprise finance data for real-time insights. Importantly, Copilot for Finance is not limited to Dynamics 365 – it can integrate with various ERPs including Dynamics 365, SAP, etc via connectors though it is deeply optimized for Dynamics 365 Finance.
The Age of AI Specialists in Microsoft 365 Copilot
Microsoft 365 Copilot is evolving from a single assistant into a team of AI specialists. Earlier this year, Microsoft announced two first-of-their-kind “reasoning agents” for work: Researcher and Analyst. After a period in preview (through the Frontier program) for early adopters, these agents are now generally available to all users with a Microsoft 365 Copilot license as of June 2025. This marks a significant expansion of Copilot’s capabilities beyond its initial skill set.
The new Researcher and Analyst are advanced Copilot modes (agents) specialised for particular scenarios – complex research and data analysis. They join other Wave 2 Copilot features (like the new Agent Store, Copilot Search, Memory, Notebooks, and image generation) that Microsoft has been rolling out to enhance the Copilot experience. Jared Spataro, Microsoft’s CMO for AI at Work, describes these agents as delivering “advanced reasoning” and notes “it really is like having a dedicated employee at your side ready to go, 24-7.” In other words, Microsoft 365 Copilot is no longer just a helpful assistant within Office apps – it can now also act as an on-demand subject matter expert that tackles higher-order tasks.
From a technology standpoint, both agents leverage the latest AI models tailored for their specific domains. They use OpenAI’s powerful models (codenamed o3-mini for Analyst, and a deep research model for Researcher) combined with Microsoft’s orchestration, search, Responsible AI, and tool integrations. This means they don’t just generate quick answers; they actually reason through problems in multiple steps, consult various data sources, and produce more comprehensive results. This blog explores each agent in detail:
Microsoft 365 Researcher Agent
Researcher is the new Copilot agent that acts as a highly skilled research assistant. It’s designed to help you tackle complex, multi-step research projects right from your Microsoft 365 environment. Researcher brings together OpenAI’s “deep research model” with Microsoft 365 Copilot’s advanced orchestration and search. In practice, this means it can scour both your organisational data *and* external sources on the web to find the information you need, synthesize it, and present insights in a coherent way.
What can Microsoft 365 Researcher Agent do?
Microsoft describes Researcher as “an agent that can analyse vast amounts of information with secure, compliant access to your work data – your emails, meetings, files, chats, and more – and the web” to deliver expert insights on demand. In simpler terms, Researcher is great at doing all the digging for information, reading it and then summarising the findings for you. Some of its capabilities include:
Multisource Information Gathering: It can search through your files, emails, SharePoint, and external online / Web sources to collect relevant data and. For example, if you’re exploring a new market or analysing a topic, Researcher will pull from both internal documents and credible websites to gather material.
Smart Summaries: After collecting information, Researcher summarises what it finds in plain, easy-to-read language. You get a clear, tailored report instead of a dump of raw data. It will highlight key points, trends, and insights rather than making you sift through hundreds of pages or search results.
Trend and Insight Identification: Researcher uses its AI reasoning to spot patterns, trends, and opportunities in the information. It can draw connections and highlight things that might make a difference for your project or question. For instance, it might notice an emerging customer preference across feedback data or identify a common thread in market research reports.
Interactive Refinement: If your initial query is broad, Researcher often asks clarifying questions to narrow down the scope and ensure it’s on the right track. This interactive back-and-forth helps it deliver more relevant results. You can guide it by answering those questions or giving additional instructions, much like you would with a human researcher.
Citations and Source Transparency: When delivering its findings, Researcher provides well-sourced content. It can include citations or references for where information came from, so you can trust but verify the results. (This is crucial for workplace research, and you can ask it to only use authoritative sources for extra confidence, as in one example prompt Microsoft shared).
Use Cases for Microsoft 365 Researcher Agent
Researcher is great in situations where you need to quickly learn or compile knowledge on a topic or subject area but are not sure where to look. This could be for tasks like assessing the impact of the new Trump tariffs on business lines, preparing for vendor negotiations by gathering supplier intel, and collecting client research before sales pitches.
Researcher Agent Example
In a business context, imagine your sales / marketing team are looking for a fresh perspective on top technology investments organisations are making in the UK based on industry research which needs to be in a report. You could ask Researcher “What are the top technology investments and projects by “small to medium” and enterprise organisations in the UK. Use trusted market data from repuatble sources such as Gartner, IDC, Cisco, Microsoft, Canlays, CRN etc.”
What I love is how you see the deep thinking and reasoning Researcher is using to compile the information and generate your report. This is so much easier than manually searching the web and reading dozens of articles. Instead, Researcher gives you a report in just a few minutes.
Instead of manually having to search the web and read loads and loads of articles, Researcher gives you a report in under ten minutes. You can of course tweak the response by asking more questions or requesting adjustments to ensure it meets you needs. When the report is finished you’ll see how comprehensive and well formatted it is, allowing you to export to, add it to a collaborative Copilot Notebook or leave it as is.
Sample output from Researcher Agent.
Microsoft 365 Analyst Agent – Data Analyst
If Researcher is your content and knowledge scout, Analyst is your number-crunching, data-savvy AI team member. The Analyst agent is all about diving into data (often numerical or structured data) to extract insights, find patterns, and answer complex analytical questions. Microsoft describes Analyst as “thinking like askilled data scientist”, using an advanced reasoning approach to tackle data problems step-by-step https://www.microsoft.com/en-us/microsoft-365/blog/2025/06/02/researcher-and-analyst-are-now-generally-available-in-microsoft-365-copilot
What makes Microsoft 365 Copilot Analyst Agent special?
The Analyst agent runs on a finely-tuned AI reasoning model (post-trained on OpenAI’s o3-mini model specifically for analytical tasks). Unlike a standard chatbot that might try to answer a data question in one go (and often make mistakes), the Analyst agent uses a chain-of-thought process to break problems down and solve them iteratively. It can even generate and execute actual code (like Python) in the background to manipulate data, perform calculations, or generate charts. Throughout this, it adjusts to new complexities and can recover from errors autonomously – essentially debugging and refining its approach as it goes, much like a human analyst would. The end result is a thorough analysis with reasoning that is transparent to the user.
Here are some of the key capabilities of the Analyst agent:
Data Analysis Across Formats: Analyst can work with Excel spreadsheets, CSV/TSV files, databases, Power BI reports, and other structured datasources . It can even extract financial data from PDFs. It is possible to upload or point it to a dataset, even if the data is messy or hidden across multiple files. For example, if you have sales data split across a few different Excel sheets and files, you can use Analyst Agent to ingest them all. The agent can also clean up many of the typical issues found in spreadsheets such as wrong delimiters in a CSV, or values buried in an unexpected place before it starts to work. This means that your data does not need to be perfectly prepared beforehand .
Iterative Reasoning and Problem Solving: When you ask Analyst a question, it will hypothesise, test, and refine repeatedly. For instance, you might ask, “What insights can you find about our Q4 sales data, and why did some teams underperform?”. Here, Analyst might break this down into steps: first identifying overall sales by region, then noticing why one sales team is lower, then digging into possible factors (maybe inventory issues or lower marketing spend), then correlating that with other data. It takes as many steps as needed to arrive at a sound answer. This multi-step approach leads to more accurate and nuanced results than a one-shot response.
Code Generation and Execution: A standout feature – Analyst can write and run Python code behind the scenes to perform calculations or data transformations. If your data question requires a formula, statistical analysis, or creating a chart, Analyst will generate the code to do it. Even better, it shows you the code in real time as it works, so you have complete transparency into how it’s reaching its conclusion. You effectively have an AI that can program on the fly to solve your data problem. This is like having a data analyst who is also a programmer working for you instantly.
Insight Generation and Visualisation: Analyst doesn’t just provide text based results – it will also explain the “story” behind the numbers in plain language and can also create simple charts or graphs to illustrate key points. It could, for example, produce a trend line graph of sales over time or a bar chart of top-performing products if those help answer your question. It will highlight findings such as “Sales Team A had a 20% increase in Q4, outpacing their previous year results ,,,, ” By narrating and illustrating the data, it helps you quickly understand the business implications.
Actionable Recommendations: Analyst can often suggest next steps or recommendations based on the data patterns it finds. If it discovers, say, that a certain region’s sales are lagging due to low inventory, it might recommend increasing stock or marketing in that region. Or if a customer segment is showing poor engagement, it could suggest targeted outreach. These suggestions turn raw analysis into useful advice, bridging the gap from insight to action.
Microsoft 365 Analyst Agent Use Cases:
The Analyst agent is useful anywhere you have data and questions about that data. Some real-world examples Microsoft has noted include using Analyst to assess how different discount levels affected customer purchasing behavior to identify the top customers who aren’t fully utilising the products they bought, and to visualise product usage trends and customer sentiment for informing go-to-market.
Analyst Agent Example
In the example below, I took some Customer Support Tickets from an excel (see below).
Sample Customer Support Ticket Export
I then have asked the Analyst Agent to “review the support ticket and create me an exective summary of the tickets, pulling out trends and themes that my team should look at and how they might reduce future support call duration.”
The results below are the first run with data that represeted as I have asked.
How Do Researcher and Analysts Agents Compare to the Standard Microsoft 365 Copilot Experience?
With all the excitement around Researcher and Analyst, you might wonder how they differ from the core Microsoft 365 Copilot Chat experience that users have been trying out in apps like Word, Excel, Teams, and Outlook.
The key difference comes down to depth of reasoning and specialisation. The core Copilot Chat experience is like a well-rounded generalist – great at everyday productivity tasks, such as drafting an email, summarising a document or thread, writing in Word, generating a PowerPoint outline, or pulling insights from a single Excel worksheet. It uses a large language model (LLM) to understand your prompt and the context from the active document, then provides a response.
However, it typically gives a direct answer or action based on available content, without doing prolonged multi-step reasoning. For example, standard Copilot can summarise a document or create a draft from prompts, but if you ask it to perform a very complex analysis that requires digging through multiple files or doing calculations, it may hit its limits. Thats where these specialist agents differ:
Advanced Reasoning vs. Quick Responses: “Standard” Copilot Chat is designed for quick assistance within the flow of work (one-shot answers or short tasks). In contrast, Researcher and Analyst use advanced reasoning algorithms (chain-of-thought) that allow them to work through a problem in multiple steps). They will plan, execute sub-tasks (like searching sources and creating and executing code), and then refining its output. This means they can handle questions or tasks that the regular Copilot would either answer superficially or not manage at all.
Tool Use and Data Access: These specialist agents have access to a much broader set of information and models. Researcher can tap into web search and internal knowledge bases simultaneously, something standard Copilot doesn’t proactively do by itself. Analyst can use the equivalent of a built-in scripting engine (Python) to manipulate data. These abilities let the agents produce more accurate, data-backed results (for instance, Analyst can compute exact figures or generate a pivot table behind the scenes, rather than guessing).
Use Case Focus: Out of the box, Microsoft 365 Copilot has a breadth of capabilities across Word, Excel, PowerPoint, Outlook, Teams, etc., but each in a somewhat scoped way – e.g. helping write, summarise, or create within that app. It is “broad but shallow”. Researcher and Analyst are narrower but much deeper in their domains. If you don’t need multi-step research or advanced data analysis, you might not need to use them and the regular Microsoft 365 Copilot Chat or in app Copilot experience might suffice. But if you do have those needs, these agents provide a level of expertise that feels like a specialist joining your team.
For example, consider interpreting a complex financial report: Standard Copilot in Excel can summarise that report or maybe answer something about it if asked directly, but Analyst could take multiple financial files (ledgers, budgets, forecasts) and do a cross-file analysis, then produce a summary and suggest optimisations – a far more sophisticated outcome.
Interaction Model:Using Researcher/Analyst is a bit like launching a specific mode of Copilot meant for heavy tasks. They’re accessible via the Copilot app’s Agent Store or as pinned which is a different entry point than simply typing to Copilot in Word. This interface guides the user to ask bigger questions (“Help me investigate X” or “Analyse Y data for Z”) rather than the smaller in-app prompts. The agents also tend to show their working process (especially Analyst showing its code or reasoning steps), whereas standard Copilot just delivers the end answer in a friendly tone. This transparency is great for users who want to trust the results – you can literally see how Analyst arrived at an answer, step by step.
Analyst vs. Copilot for Finance – What’s the Difference?
With the introduction of the Analyst agent, you might also hear about Microsoft 365 Copilot for Finance – another AI offering that targets data and analytics, but specifically for finance professionals. It’s important to clarify how the Analyst agent and Copilot for Finance differ, because their names might seem related. In fact, they serve different needs:
Microsoft 365 Copilot for Finance (formerly introduced simply as “Copilot for Finance”, now in preview) is a role-based Copilot experience tailored for finance departments. This was announced in early 2024 as a way to “transform modern finance” by bringing generative AI into the daily workflows of finance teams. Unlike the Analyst agent – which any user with Copilot can use for various kinds of data analysis – Copilot for Finance is a separate add-on Copilot designed to integrate deeply with financial systems and processes. It essentially combines Microsoft 365 Copilot with a specialized finance agent and connectors to your financial data.
From what I have managed to assess these are the main differences between the Analyst agent and Microsoft 365 Copilot for Finance:
Aspect
Analyst Agent (Microsoft 365 Copilot )
Microsoft 365 Copilot for Finance
Purpose & Domain
General-purpose data analysis for any domain or department. Helps users analyse spreadsheets, databases, or other data to get insights.
Designed to work across certified and connected systems such as Microsoft 365 Dynamics, Salesforce and some others
Integration and Data
Works on provided or accessible data in Microsoft 365 (e.g. Excel files, CSVs, SharePoint data). No built-in direct connection to ERP systems – user typically uploads data or points to files for analysis
Connected to enterprise financial systems and data sources. Draws context from ERP systems (like D365 Finance & SAP) and the Microsoft Graph . Integrates in real-time with live finance data, assuming connectors are set up. Optimised for D365 Finance (seamless data access). Can connect other systems via custom or pre-built connectors).
Features and Skills
Uses chain-of-thought AI reasoning and Python code execution to perform analytics. Ideal for ad-hoc data analysis: e.g. combining sales data with customer data to find trends, identifying anomalies in operational data, generating charts from raw data. Acts as AI data analyst for any project.
Uses AI to streamline finance-specific processes and provide insights within finance workflows. For example, can automate variance analysis in Excel, perform reconciliations between systems, generate reports, summaries, and even draft emails for collections with relevant account info. Understands accounting principles and the company’s financial data.
User Experience
Accessed through the Copilot app as one of the agents (no special deployment beyond having Microsoft 365 Copilot license). The user asks questions or tasks in natural language and often provides the data files to analyze. The output is an interactive analysis in Copilot chat with optional visuals and code transparency.
Integrated into the tools finance teams use: primarily Excel, Outlook, and Teams in the context of finance work. For example, in Excel a finance user might invoke Copilot for Finance to run a budget vs. actual report or find anomalies in ledger data. In Outlook, it can summarise a customer’s account status from ERP data to help a collections officer. Works in flow of existing finance tasks, bringing AI where needed.
Availability & Pricing
Included as part of the Microsoft 365 Copilot (the Analyst agent is available to any user who has Copilot enabled). General Availability as of mid-2025. Usage is capped at 25 queries/month for heavy reasoning tasks.
Available as add-on to Copilot targeted at enterprises. Paid offering for organisations that use Microsoft 365 and want AI assistance in finance for supported systems like D365.
Dependencies on Microsoft Dynamics
Not dependent on Dynamics 365 – Analyst can analyse any data you give it. If your financial data is in Excel exports from SAP or Oracle, Analyst can still work with those exports, but it won’t directly pull from those systems on its own.
Deeply integrates with D365 Finance & Operations. Designed to plug into D365 modules so can act within that ecosystem (e.g., directly reading transaction data, posting results back). Through “connectors”, it can interface with other ERP or CRM systems too. Advantage is native use with D365 – without manual data exporting or integrations
To put it simply, the Analyst agent is like an AI data expert you can use for virtually any type of analysis by feeding it data, whereas Copilot for Finance is a comprehensive AI-powered solution built into Microsoft’s ecosystem to assist with a company’s financial operations in real-time. They might overlap in the sense that both can do things like variance analysis or finding trends in financial figures, but the context is different: Analyst would do it when you ask and give it the data (say, a couple of Excel files containing financial info), while Copilot for Finance would do it as part of your normal finance workflow, already knowing where the data is (in your ERP and Excel models) and proactively helping you in that domain.
Does Copilot for Finance only work with Dynamics 365?
No. Copilot for Finance is not limited to Dynamics 365, though that’s a primary integration. It brings together Microsoft 365 Copilot with a finance-focused agent that connects to your existing financial data sources including ERP systems like Dynamics 365 and SAP. So if your company runs SAP for finance, Copilot for Finance can use that data as well. Microsoft has built it to be flexible via connectors, because they know not everyone is on Dynamics. That said, organizations using Dynamics 365 Finance get a more seamless experience – Copilot for Finance can sit right inside the D365 Finance interface and offer insights without any data transfer.
In summary, Copilot for Finance is cross-platform in terms of data sources, but tightly integrated with Microsoft’s own finance solutions for maximum benefit. It’s an example of Microsoft creating role-specific Copilots (others being Copilot for Sales, Copilot for Service) that extend the core Copilot capabilities into specialised business functions.
Further Reading and Sources
As well my own experimentation, the following sources were also inferred and read when writing this blog. I did also use Copilot to help tweak the tone and flow.
Microsoft has introduced autonomous Copilot AI agents in public preview. These agents can learn, adapt, and make decisions, aiming to assist employees with various tasks and improve productivity. While AI has the potential to displace some jobs, it also creates new opportunities and enhances productivity.
Microsoft’s wave of Autonomous agents are here
Microsoft has unveiled new tools designed to help businesses create software agents powered by foundation models, referred to as autonomous Copilot AI agents. These agents are currently available in public preview.
Copilot is Microsoft’s generic term for all their AI-driven productivity workloads. Copilot is built upon the advanced GPT-4 series of large language models by OpenAI and offers a chatbot interface where users can input text, images, or audio prompts to receive responses tailored to their needs. Microsoft 365 Copilot also seamlessly integrates with Microsoft Office applications like Word, Teams, and Excel. It can generate documents, analyse extensive Excel spreadsheets, summarise meetings content, rewrite documents, create entire PowerPoint presentations and even reason over your inbox and company information you have access too….., and much, much more.
The next step in Microsoft 365 Copilot’s advancement is through what are termed AI-Agents, which are chat bots that can not only respond but can also perform a series of linked tasks (actions) based on user instructions. This new wave went into public preview this week at Microsoft Ignite in Chicago.
What are Microsoft 365 Copilot Agents?
This first stage of the next phase of evolution comes with Microsoft introducing a set of Microsoft 365 Copilot agents with predefined roles. These include:
Agents in SharePoint. These can be customers with a personalised name and certain behaviours, and can be shared across emails, meetings and chats, with users being able to ask the agents questions and getting real-time responses. These are grounded just on the SharePoint sites and files you specify. One created, employees can ask the agents questions about data across your files. These agents can even be shared or published in Teams for simple access.
The Employee Self-Service Agent in Microsoft 365 Copilot Business Chat (this currently in private preview), will be able to respond to specific HR and IT questions. It can retrieve employee benefits and even things like payroll info and holiday information, or request help from IT such as a new mouse, password reset etc.
The Facilitator agent (in public preview), works like a assistant in meetings and goes beyond the current AI notes that Teams Premium offers. It can take notes, curate actions and even pull up information or execute instructions such as “see if Bob is free and invite him to the meeting”. It will also be able to summarise the conversations based on the role of the participants.
The Interpreter agent (due in preview in early 2025) promises real-time interpretation in Teams meetings in up to nine languages. It will also be able to sample and then simulate their personal voice for a more inclusive experience as part of the translation, essentially using the sound of your voice in the language of the other participants. It was great to see this in action at ignite in a live demo!
The Project Manager agent, will be able to act and work like a PM with the ability to automate project management, from planning to execution using Microsoft (and later other) project tools like Planner.
For organisations that need more control or different templates to build on and use, Microsoft Copilot Studio provides a way to customise or create your own AI agent behaviour.
Agents in Copilot Studio
Agents built in Copilot Studio can operate independently, dynamically planning and learning from processes, adapting to changing conditions, and making decisions without the need for constant human intervention,These autonomous agents can be triggered by data changes, events, and other background tasks – and not just through chat.
Copilot Studio bundles many templates for common agent scenarios that can serve as the basis for a customised version. It will also shortly support voice-enabled agents, image uploading (for analysis by GPT-4o), and knowledge tuning with the added ability automatically add new sources of knowledge to help agents respond to questions.
Devs can use the Agent SDK to access services from Azure AI, Semantic Kernel, and Copilot Studio. There’s also an Azure AI Foundry (also launched at Ignite) integration that links Copilot Studio to facilitate connection to services like Azure AI Search and the Azure AI model catalog.
Finally, a public preview of agent builder in Power Apps was also announced at Ignite.
What about Responsible AI?
Sarah Bird, chief product officer for Responsible AI, wrote in a blog post this week that extra safety considerations arise with autonomous agents and that Microsoft is focused on ensuring that they behave and hand over to human before taking unexpected actions which can have big impacts and that extra guard rails and protections will be put in place.
The blog post talks about examples of such measures including the vital need for a human-in-the-loop check to make sure autonomous decision-making doesn’t do things it’s not expected too. Nothing demonstrates confidence in automation more than a human approval process.
Microsoft also suggest that anyone looking to get a sense of AI agents in a real role, can try out the Linked In Hiring Assistant which is designed to help HR hiring teams speed up the process of dealing with the Admin involved in reviewing job applications.
Key Benefits
The key Benefits these new adaptions to Copilot. Agents should bring to users and organisations includes:
Learning and Adaptation: The Copilot AI agents can learn from their environment and adapt to new information and tasks.
Decision-Making: These agents are capable of making decisions to assist users in their daily work.
Productivity Enhancement: The primary goal is to empower employees by reducing workload and improving efficiency in tasks such as managing meetings, emails, and creating presentations.
Automation of some tasks connected to regular and recurring inquiries or asks.
Human Impact – what about jobs!
The introduction of AI and automation, including Microsoft’s Copilot AI agents, has the potential to impact the roles of people in jobs.
Job Displacement: People naturally worry that AI has the potential to replace certain jobs, particularly those involving repetitive and manual tasks. According to a report by Goldman Sachs, AI could replace the equivalent of 300 million full-time jobs….. But.
Job Creation: On the other hand, AI also creates new job opportunities. It can lead to the emergence of new roles that require advanced technical skills and the ability to work with AI systems
Economic Impact: AI is expected to contribute significantly to global economic growth. McKinsey Global Institute estimates that AI could deliver additional global economic activity of around $13 trillion by 2030
Skill Demand: The demand for skills will shift towards more advanced and technical capabilities. Employees will need to upskill and reskill to stay relevant in the evolving job market. AI skills will be similar requirement to the “Internet skills” we saw on CVs in the 1990s!
Conclusion
Microsoft’s autonomous Copilot AI agents represent a significant step towards integrating advanced AI into everyday business operations. By enhancing productivity and reducing routine workload, these agents have the potential to transform how employees manage their tasks.
These will be in public preview very soon as these often take a few weeks to rollout across the globe.
Source: Conversation with Copilot, 22/11/2024 (1) How Will Artificial Intelligence Affect Jobs 2024-2030. https://www.nexford.edu/insights/how-will-ai-affect-jobs.
(3) The impact of AI on jobs – GOV.UK. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1023590/impact-of-ai-on-jobs.pdf.
In our recent fireside chat, we delved into the transformative potential of AI agents across multiple industries. These business areas include customer service, IT support, and internal business support. The discussion, titled “Rise of the AI Agents,” brought together industry experts across several fields. These included transportation, public sector, legal, media, and executive search. The panel explored how AI is reshaping customer and consumer interactions and discussed enhancing efficiency and driving more inclusive interactions.
Introduction to AI Agents
We kicked off the session our fireside chat by setting the scene. We highlighted the traditional challenges faced in contact centers. These include long hold times and inefficient call transfers between chat bots and human agents. Here we agreed on these and but also the importance of not just jumping on “injecting ChatGPT” into workflows, but instead discussed the advent and value of generative AI and human-like conversation across chat and AI-Voice and how these rapid technology advancements have the potential to revolutionise these experiences.
AI agents, leveraging large language models like, are now capable of understanding context, handling a wide range of queries, and providing personalized responses and we are seeing Contact Center solutions such as Cisco Webex, starting to infuse this technology to assist end-to-end in the Human-to AI, Human-to-AI-to-Human, and Human-to-Human conversation.
AI agents leverage advanced technologies like large language models (LLMs) and machine learning to provide more dynamic and context-aware interactions. AI agents can understand and generate natural language. This ability allows them to handle a wider range of queries. They also provide more personalised responses. They can learn from data and feedback, improving their performance over time without needing manual updates.
AI agents can also integrate with various data sources and systems, enabling them to provide more comprehensive and accurate information.
Autonomous Agents can operate entirely independently, without human intervention. They can handle multi-step tasks, make decisions based on pre-programmed logic, and adapt to new situations using advanced AI techniques like reinforcement learning. These agents are ideal for environments where human input is minimal or impractical.
Semi-Autonomous Agents on the other hand, still involve a “human in the loop.” While they can perform many tasks independently, they require human input for certain decisions or actions. This hybrid approach combines the efficiency of automation with the oversight of human expertise, ensuring accuracy and reliability.
The example below is a recent marketing video from Cisco introducing their new AI agent in Webex Contact Centre.
AI Agent example in Cisco Webex Contact Centre
AI Agent – Use Cases
Through the discussion, the panel agreed on several key areas in which AI assisted agents could add value.
Customer Support: Investing (or extending existing platform) in AI agents can help multiple lines of business better and more efficiently handle routine customer inquiries, such as changing addresses, booking or changing appointments, and freeing up human agents to work on less trivial customer requests or more complex issues.
Sales Assistance: Another area discussed, was where AI can assist human agents (for example in sales or customer service), by providing real-time information and suggestions during human customer interactions, improving the chances of successful sales conversations, such as overcoming objections or asking for more technical information about a product or service.
Customer Service and Complaints: helping agents improve their interaction with their customers, such as making agents aware of similar problems, outages or similar calls that led to successful outcomes or helping explain something better or in a different way to their customer.
Training and Development: AI can be used to train new agents by simulating customer interactions and providing feedback, helping them improve their skills more quickly. This can be used for onboarding fresh staff, running different customer scenarios or reviewing previous calls for improvement
Sentiment Analysis: Using AI to analyse customer sentiment during interactions, allowing agents to adjust their approach and improve customer satisfaction as well as flagging to supervisors early where interaction or training may be needed.
AI Agent Value and Applications
Driving efficiency and improving satisfaction
Darren Everden (London Borough of Hillingdon) shared his insights on how local authorities are looking at utilising AI to improve resident interactions. David emphasised the importance of channel shift and transformation in the public sector, driven by funding reductions and the need for more cost-effective solutions that also improve the resident experience and resolution rate. Darren highlighted the evolution of chatbots, which can now use natural language processing to understand and respond to resident queries more effectively. He also discussed the potential of integrating AI into voice channels, enhancing accessibility and providing a more natural interaction experience making it almost impossible to differentiate from human voice. Interactions are far more natural than ever, and this continues to evolve and improve with models such as ChatGPT-4o.
Inclusivity and Accessibility
Ken Dickie from Leathwaite Executive Search, discussed the role of AI in promoting and improving inclusivity and accessibility. He pointed out that AI agents are far better at being able to adapt to the needs of users with disabilities, such as dyslexia, by adjusting text spacing or providing audio responses something human only operated agents simply cannot easily do This real-time adaptability empowers individuals to engage with systems more effectively. Ken also mentioned the global reach of AI, enabling organisations to provide support in multiple languages, thus breaking down communication barriers.
Enhancing Agent Efficiency
Aidan Shanahan from Govia Thameslink Railway discussed the benefits of AI in assisting human agents. He discussed his view on where AI can provide real-time guidance and sentiment analysis, helping agents handle customer interactions more effectively. The panel here discussed the role AI as an human assistant (An Agent to the Agent) being particularly valuable in high-stress situations, such as handling complaints, where AI can suggest appropriate responses based on the customer’s tone. Aidan also highlighted the potential for AI to improve internal processes, such as IT support, by automating routine tasks and reducing response times, replacing laboreous processes with natural language requests.
Jas Bassi from Gately highlighted the potential applications of AI in the legal sector. While acknowledging the generational differences in adopting new technologies, Jas emphasized the need for a multi-channel approach that includes both human and AI interactions. He pointed out that AI can deliver efficiency gains in transactional activities, ensuring faster and more consistent service delivery. However, he also raised concerns about biases in AI training and the risk of deep fakes, underscoring the importance of maintaining a balance between automation and human oversight.
Low cost of entry and ease of Proof of Concept
Alex Taylor from Awin shared his experience with implementing AI agents internally at Awin. He mentioned that this is no longer about one off business cases and specific dictated expensive systems. He shared that he is seeing huge interest in the use of AI agents across various departments, such as InfoSec and marketing, in leveraging AI to not only automate and ease customer interactions but also going beyond this and automating processes and improving efficiency. He emphasised the importance of extending this value by connecting backend systems (which also involved in many cases minimising diserpate vendrs) and ensuring they are “compatible” to maximize their effectiveness with automation and semi-automatic interactions. He realised examples, of automatically logging tickets, providing simple answers to issues and even liaising with other systems or processes.
Finally Alex and Ken agreed that the bar to entry is much lower, with a similar approach,. bring able to serve multiple departments, handle thousands of enquiries and not only reduce the cost, but truely delivery faster, more inclusive and international support even for organisations that don’t have global offices.
Conclusion: The Value and Opportunities of AI Agents
Our fireside chat concluded that there were several key value points when it comes to the use and exploration of AI agents across customer and employee focused formal contact centers but also across more adhoc and internal communications within and across business, from website chat to internal IT support.
Enhanced Customer Interaction: AI agents can provide more efficient and personalized customer service, reducing wait times and improving satisfaction
Cost-Effective Solutions: The low cost of entry and easy of deployment (compared to the previous laborious process of programming conversational paths), enables organisations to handle a higher volume of interactions without significantly increasing costs, making it a viable option for sectors with budget constraints and most importantly without a huge development and support burden.
Inclusivity and Accessibility: AI agents can adapt to the needs of diverse user groups, promoting inclusivity and breaking down language barriers both with end customers and with human-agents.
Supporting the Human Agents: AI assists can act as a huge support for human agents by providing real-time guidance, sentiment analysis, and automating routine tasks, enhancing overall efficiency and can even help handle delicate situations, detect agent stress and suggest rest-bites and training to supervisors based on AI assisted analysis.
Internal Process Optimisation: Used effectively, this can extend way beyond the conversation, streamlining internal workflows, reducing response times and improving productivity across various departments.
