Building Custom Agents and Topics in Microsoft Copilot Studio

What Microsoft Copilot Studio actually is

Microsoft Copilot Studio is Microsoft's graphical, low-code tool for building agents and agent flows. Microsoft's own documentation defines it plainly as 'a graphical, low-code tool for building agents and agent flows,' and its standout capability is the ability to connect to other data sources using prebuilt or custom connectors so you can orchestrate sophisticated logic without writing code. You access it as a standalone web app at copilotstudio.microsoft.com, and the agents you build live inside a Power Platform environment backed by Dataverse.

The distinction that matters for a Dynamics 365 customer is what Copilot Studio is not. It is not the Copilot experiences embedded inside each Dynamics 365 app (Sales, Customer Service, Field Service, Supply Chain Management, Business Central, Finance) — those ship with the apps and are configured, not built. Copilot Studio is where you go when the included Copilot does not cover a scenario and you need to build a custom agent: one that knows your specific business data, takes actions in your systems, and reaches users on the channel you choose.

Practically, Copilot Studio sits in the same family as the broader Microsoft Copilot surface, but it is a maker tool, not a consumer feature. It is aimed at IT admins, makers familiar with agent services, and developers who want to assemble complex agents using entities, variables, topics, and tools — or to extend Microsoft 365 Copilot with enterprise data the stock experience does not handle. If you are evaluating where Copilot fits across your estate, read the platform-wide overview first; this guide assumes you already know what Copilot is and focuses on building with it.

Agents, topics, and flows: the three things you build

Copilot Studio organises work around three building blocks, and confusing them is a common beginner mistake. An agent is the AI companion itself — it coordinates language models with instructions, context, knowledge sources, topics, tools, inputs, and triggers to resolve problems that can require complex, multi-turn conversations. A topic is a portion of a conversation between a user and the agent, defined on a canvas as a sequence of nodes. A flow is an automation that runs repetitive tasks and integrates apps and services, triggered manually, on a schedule, by events, or by the agent itself.

The relationship is hierarchical and worth memorising. An agent contains topics and can call flows. Topics contain nodes, and some of those nodes are tool nodes that invoke flows or connectors. Flows can also run standalone, outside any conversation. So when you plan a build, you decide the agent's scope first (what it knows and does), then break its behaviour into topics (one per intent or task), and reach for flows only when a topic needs to take a real action in an external system — creating a record, sending an email, running a calculation.

Microsoft also distinguishes two flow formats. Agent flows are the existing format, with an authoring experience similar to Power Automate but native to Copilot Studio; workflows are a newer format in public preview with a revamped visual designer and improved testing. You can run either as a standalone automation or configure one to trigger from an agent as a tool and return results to that agent. Crucially, flows can run prompts, call other agents, and include human-review steps — which is how you keep a human in the loop on anything sensitive.

How to create an agent from a plain-language description

Creating an agent in Copilot Studio is deliberately approachable: you describe the agent you want in plain language, telling it what instructions, triggers, knowledge sources, and tools it should have, then test it before you deploy. The platform's low-code experience is designed to put AI authoring at your fingertips without an extensive technical background, and you can iterate by describing the changes you want rather than editing configuration by hand. For a Dynamics 365 team, this means a functional agent can be standing in a test panel within an hour.

Under the hood, an agent is more than a prompt. It coordinates a language model with a set of declared components: instructions (the system behaviour), knowledge sources (where it grounds answers), topics (deterministic conversational paths), tools (actions it can take), inputs and outputs, and triggers (what starts it). When you describe an agent in plain language, Copilot Studio drafts these components for you, but you are expected to refine each one. The quality of an agent is bounded by the quality of its instructions and the relevance of its knowledge — a vague instruction set plus a noisy knowledge source produces a confident-sounding agent that hallucinates.

Microsoft is explicit about the two creation paths. You can build a standalone custom agent when you want something embedded in your own web or mobile app with full control over branding and orchestration, or you can extend Microsoft 365 Copilot with an agent when you want to declare instructions, tools, and knowledge that reuse the existing Copilot orchestrator. The extend path is lighter-weight and lands inside Microsoft 365; the standalone path is right when the agent must live in a customer-facing surface or a non-Microsoft channel.

Topics: trigger phrases, nodes, and the authoring canvas

A topic defines how an agent conversation progresses, and you author it on a canvas by adding nodes. Each node performs an action: a Message node sends text, a Question node asks for input, an Adaptive Card node shows an interactive card, a Condition node branches the conversation, and tool nodes call a flow, a connector, or another tool. A topic also has a Trigger node that decides when it runs. Microsoft recommends five to ten trigger phrases per topic to train the natural-language model, and trigger phrases can be uploaded or downloaded as a text file (one phrase per line, up to 3 MB).

Every new agent starts with a set of predefined system and custom topics. System topics support essential behaviours such as escalating to a person or ending the conversation — you cannot create or delete them, but you can turn them off and edit them. Custom topics cover common behaviours like greeting a customer or restarting a conversation, and everything you create yourself is a custom topic. The practical guidance is to leave system topics alone until you are comfortable building complete agent experiences, because a broken greeting or escalation topic damages every conversation.

Two authoring shortcuts matter for productivity. First, topics have input and output parameters, so when one topic redirects to another you can pass information between them — and under generative orchestration the agent can automatically fill topic inputs from conversation context. Second, there is a code editor that shows a topic as YAML, which is useful for copying topics between agents or version-controlling them. Microsoft cautions that designing an entire topic in the code editor and pasting complex topics is not fully supported, so treat YAML as a portability and inspection tool, not the primary authoring surface. One hard rule from the docs: avoid periods in topic names, because you cannot export a solution that contains an agent with periods in any topic name.

Generative vs classic orchestration: the decision that shapes everything

The single most important configuration choice in Copilot Studio is the orchestration mode, because it changes how the agent selects topics, searches knowledge, calls tools, and responds. Agents can use either generative or classic orchestration, and by default newly created agents use generative orchestration. An admin can turn off generative orchestration for an environment, in which case agents created there can only use classic.

Under classic orchestration, the agent picks the topic whose trigger phrases most closely match the user's query, falling back to knowledge as a fallback when no topic matches. Tools can only be called explicitly from within a topic, and you must author Question nodes to prompt for missing information and Message nodes to respond. It is deterministic and predictable, and it is the right choice when you want tight control over conversational paths — for example, a regulated intake flow where every branch is audited.

Under generative orchestration, the agent uses a GPT model to choose the best combination of topics, tools, knowledge sources, and other agents to answer a query or respond to an event. It selects topics based on their description (not trigger phrases), can call tools based on their name and description, can proactively search knowledge, can chain multiple topics and tools together to handle multi-intent queries, and can automatically generate questions to fill missing inputs. The trade-off is flexibility and naturalness against determinism: generative agents feel more conversational and handle unpredictable phrasing, but their behaviour is probabilistic and harder to audit step by step.

Why descriptions are the new trigger phrases

When generative orchestration is on, the most important authoring task stops being trigger phrases and starts being descriptions. The agent selects topics, tools, other agents, and knowledge sources primarily based on the description of each one, with secondary signals from names, input/output parameters, and their descriptions. Microsoft's guidance is blunt: good descriptions ensure the agent selects the right components to respond to users, and vague or overlapping descriptions are the leading cause of an agent invoking the wrong topic or tool.

The documented best practices are worth following literally. Use simple, direct language in the active voice and present tense — write 'This tool provides weather information' rather than 'Weather information is provided by this tool.' Use keywords that relate to the functionality and user intent. Limit the description to one or two sentences that explain what the component does and how it benefits the user. Use a descriptive, unique name that is a short phrase ('Weather Forecast' rather than 'Weather'). And make similar topics explicitly different — indicate what each one cannot do, so the agent can disambiguate between 'Current Weather' and 'Weather Forecast for Tomorrow.'

There are real gotchas. If multiple topics have similar descriptions the agent may invoke all of them to answer a single question, so test thoroughly and revise overlapping descriptions. Custom entities (closed lists and regex entities) are not yet supported as input parameters for topics and tools under generative orchestration — to collect information using a custom entity, use a Question node inside a topic. And the agent's access to conversation history is currently limited, so on long conversations it may need to re-ask for information that appeared earlier.

Knowledge sources: grounding agents in Dataverse and SharePoint

Knowledge sources are how an agent stops guessing and starts answering from your data. In Copilot Studio, knowledge sources work with generative answers so agents can use enterprise data from the Power Platform, Dynamics 365, websites, and external systems. You can add knowledge at the agent level (on the Knowledge page) or at the topic level (via a generative answers node), and a published agent that contains knowledge is grounded by the configured sources. This is the layer that makes a Dynamics 365 agent genuinely useful, because Dataverse is a first-class knowledge source.

Microsoft supports five knowledge source types, each with its own limits and authentication model. Public websites are searched via Bing and return results only from the URLs you provide. Documents are uploaded to Dataverse and searched by content. SharePoint connects to a URL and uses Graph Search. Dataverse connects to your environment and uses a retrieval-augmented generation technique to return results. Enterprise data via connectors reaches organisational data indexed by Microsoft Search. For SharePoint, Dataverse, and connectors, the agent uses the end user's Microsoft Entra ID authentication, so each user only sees content they are allowed to access — a critical security property for any CRM or ERP scenario.

The limits differ sharply between orchestration modes, and this is where SMEs get caught. In classic mode, the Conversational boosting system topic is capped: four SharePoint URLs, four website URLs, two Dataverse sources with up to 15 tables each, and so on. In generative mode the limits are far more generous — unlimited Dataverse, unlimited connector-based enterprise data, 25 websites, 25 SharePoint URLs — and when there are more than 25 sources the agent uses an internal GPT model to filter them by description. Generative mode does not support custom data or Bing Custom Search as knowledge sources; to use those you must embed them in a generative answers node inside a topic.

Tools and connectors: making the agent do real work

Knowledge lets an agent answer; tools let it act. In Copilot Studio, tools are how an agent takes a real action in an external system — creating or updating a Dynamics 365 record, running a Power Automate flow, calling a connector, executing a prompt, or invoking another agent. The tool node types listed in Microsoft's documentation include calling a flow like Power Automate or Excel Online, or using a connector or another type of tool, and there is a shared Tools page that lets you define a tool once and reuse it across agents.

How tools are invoked depends on orchestration. Under classic orchestration, tools can only be called explicitly from within a topic — you wire them into a deterministic path. Under generative orchestration, the agent can choose to call tools based on their name and description, automatically generating questions to gather any missing inputs (generative slot filling). This is powerful but means your tool descriptions must be precise: the agent will call a tool when its description matches the user's intent, so a poorly described tool gets called at the wrong time or not at all.

For Dynamics 365 builds, the practical connector patterns are repeatable. Use the Dataverse (Microsoft Dataverse) connector or agent flow actions to create, update, or retrieve accounts, contacts, leads, opportunities, cases, and work orders under the end user's Entra identity so row-level security still applies. Use Dynamics 365 Sales / Customer Service / Field Service connectors when you need app-specific actions beyond generic Dataverse CRUD. Call Business Central APIs through custom connectors or HTTP request nodes for BC-specific documents (sales orders, item availability) when the agent lives outside the BC embedded Copilot. Prefer a Power Automate or agent flow with an approval step for anything that posts to the ledger, closes a case, or changes pricing — keep the agent fast on triage and drafting, keep humans on irreversible writes.

Additional tool patterns that show up in real SME agents: a connector that fetches real-time data from an external API (inventory, shipping, pricing engines); a prompt tool that runs a templated LLM call for drafting emails or summarizing long case notes; computer-use tools (now generally available) when the system of record has no API and the agent must drive a browser or desktop UI; and agent-to-agent (A2A) handoffs when a front-door agent routes specialized work to domain agents. Because flows can include human-review steps, the recommended pattern for high-stakes actions is always agent proposes → flow holds for approval → system commits.

Channels: publishing to Teams, the web, and beyond

Once an agent works in the test panel, you publish it to one or more channels. Microsoft documents that agents can engage with customers and employees in multiple languages across websites, mobile apps, Facebook, Microsoft Teams, or any channel supported by the Azure Bot Service. The publishing model is write-once-deploy-many: you build and test the agent once, then configure the channels you need, which lets a single agent serve an internal Teams audience and an external web audience from the same definition.

The Teams path deserves attention because it is the lowest-friction internal deployment. With the Teams plan available in select Microsoft 365 subscriptions, you can create agents that use classic orchestration and publish them to Microsoft Teams only — a fast way to distribute an internal assistant without standing up infrastructure. Note Microsoft's documented sunset: after the end of June 2026, the Copilot Studio for Teams app can no longer create classic chatbots and will redirect makers to the Copilot Studio web app, so plan new internal agents against the web app.

For external or custom surfaces, you embed the agent in your own website or application using the provided web channel or the Direct Line channel from the Azure Bot Service. This is the path when the agent must be customer-facing — an e-commerce support bot, an appointment-scheduling assistant, or an agent embedded in a bespoke internal tool. Authentication matters here: if the agent uses SharePoint, Dataverse, or connector knowledge, you must configure user authentication so each end user only sees the data their Microsoft Entra identity permits. Publishing without authentication on a grounded agent is a common and serious configuration mistake.

Pricing: $200 capacity packs, pay-as-you-go, and worked credit math

Copilot Studio is not a single flat seat for every scenario — it combines a tenant license path with consumption metered in Copilot Credits, and this is where Dynamics 365 customers get surprised at month-end. Microsoft sells Copilot Studio as a tenant-wide license that includes Copilot Credit capacity packs of 25,000 credits each at US$200.00 per pack per month. Pay-as-you-go via Azure is US$0.01 per credit with no up-front commitment. Prepaid packs work out to about US$0.008 per credit (roughly 20–25% cheaper than PAYG when you use the pack fully). Unused prepaid credits do not roll over month to month. A free Copilot Studio user license is still required for each maker who builds and manages agents.

Microsoft documents billable rates on the standard harness. Classic answers cost 1 Copilot Credit; generative answers cost 2; agent actions cost 5; tenant graph grounding for messages costs 10; agent flow actions cost 13 credits per 100 actions; and text/generative AI tools range from 1 credit (basic, per 10 responses) through 15 (standard) to 100 (premium, per 10 responses) — with premium also used for reasoning-model token burn. Voice has separate per-minute tiers (classic voice 10, GenAI voice 35, premium GenAI voice 75 credits per minute). Critical inclusion: employee-facing (business-to-employee) usage of agents by a user licensed with Microsoft 365 Copilot, when the agent runs under that authenticated user's identity, is typically no charge against credits for core answer and action features within fair-use limits. Computer-using agents (CUA) are not included in that Microsoft 365 Copilot USL inclusion. External users, unlicensed internal users, and external channels burn credits.

Work the math before you publish. Microsoft's own examples: a website support agent averaging four classic answers and two generative answers across 900 customers per day uses [(4×1)+(2×2)] × 900 = 7,200 credits per day — more than one 25,000-pack every four days at that volume. A sales performance agent with four generative answers and four tenant-graph groundings for 100 unlicensed users costs [(4×2)+(4×10)] × 100 = 4,800 credits per day. An autonomous order-processing agent that fires four agent actions per order costs 20 credits per run. Use the official Microsoft Copilot Studio agent usage estimator to model traffic, knowledge, tools, and orchestration before go-live. When prepaid capacity hits about 125% overage, Microsoft can disable custom agents until you reallocate capacity, buy more packs, or enable PAYG.

Three purchase paths cover most estates. (1) Microsoft 365 Copilot seats: licensed users can build and use internal agents inside Microsoft 365 at no additional credit cost for included scenarios — but publishing to external channels and serving unlicensed users needs standalone Studio capacity. (2) Prepaid capacity packs: US$200/month for 25,000 tenant-pooled credits, stackable, best when volume is predictable. (3) Azure pay-as-you-go at US$0.01/credit: no commitment, auto-covers overflow, ideal for pilots and spiky external traffic. There is also a pre-purchase plan (Copilot Credit Commit Units) for annual credit commits with reported discounts up to about 20%. Budget license-plus-consumption together — never license alone.

• →Pack: $200/month for 25,000 Copilot Credits; PAYG: $0.01/credit via Azure; packs ≈ $0.008/credit when fully used
• →Classic answer 1 · Generative answer 2 · Agent action 5 · Tenant graph grounding 10 credits (Microsoft Learn rates)
• →M365 Copilot-licensed users: B2E agent usage under their identity often no-charge for core features (fair use); CUA excluded
• →Overage: enforcement can disable custom agents around 125% of prepaid capacity — enable PAYG before peak season
• →Model volume with Microsoft's agent usage estimator before switching production agents on

Governance: DLP, environments, authentication, moderation, and audit

Building the agent is the easy half; governing it is what determines whether it ships safely. Copilot Studio exposes several settings that control how aggressively the agent answers and how grounded those answers are. Content moderation can be set at the agent level, the topic level (in a generative answers node), and the prompt level (in a prompt tool), with topic-level settings taking precedence at runtime. Moderation ranges from Lowest (most answers, more risk of harmful content) to Highest (fewer answers, stricter filter); the default is High.

The 'Allow ungrounded responses' setting is the one to understand before launch. When it is off and the agent has generative orchestration on, the agent blocks any response generated in a turn where it did not use a knowledge source or tool — even a follow-up answered from conversation history. This is strict by design: it forces every answer to be grounded. The trade-off, which Microsoft documents honestly, is that the model sometimes generates a correct answer from a knowledge source but omits the in-text citation, in which case the agent withholds the answer as if it found nothing. To make grounded answers more consistent, add citation instructions to the agent and include ContentLocation and Title fields in any custom data source.

Data Loss Prevention is no longer optional. Since early 2025, data policy enforcement is in effect for all tenants; agent data-policy exemption is no longer supported, and previously exempted agents are subject to enforcement. Admins configure Copilot Studio and Power Platform data policies in the Power Platform admin center to govern maker/user authentication, knowledge sources, actions and connectors, HTTP requests, channel publication, Application Insights, and triggers. If an agent violates the environment's data policy, makers see a publish-blocking error. Design DLP before the first agent: separate Default (restrictive) from a managed maker environment (connectors you intentionally allow) and an external-facing environment (anonymous web channel, tighter outbound rules).

Beyond answer quality, treat these controls as non-negotiable for Dynamics 365 agents. (1) User authentication: any agent grounded in SharePoint, Dataverse, or connectors must authenticate so each user only sees permitted content. (2) Environment architecture: keep agents in the same Power Platform environment as their solutions and Dataverse data; use Managed Environments / environment groups for production isolation. (3) Identity: Microsoft Entra Agent IDs (preview path rolling through 2026) let admins scope connector permissions, Conditional Access, and DLP to individual agents rather than a shared maker identity. (4) Audit and inventory: use the agent inventory schema and Power Platform admin center analytics for sessions, autonomous runs, and readiness/status; session transcripts and evaluation runs support review before scale. (5) Responsible AI: Microsoft warns that Copilot can give incorrect responses; treat agent output as a starting point a qualified human reviews before it posts, resolves, or advises.

Copilot Studio vs Microsoft 365 Copilot seats vs classic topic bots

Three surfaces get confused in every budget meeting: the Microsoft 365 Copilot seat (per-user productivity Copilot in Office, Teams, and Chat), Copilot Studio as a maker platform (custom agents, topics, tools, external channels), and classic topic-driven bots (deterministic trigger-phrase paths, often the older Teams-plan or classic-orchestration pattern). They solve different jobs. A seat multiplies an individual knowledge worker. Studio multiplies a process you design once and deploy to many users or channels. A classic topic bot is Studio (or a subset of it) constrained to authored paths when you need auditability over flexibility.

Choose Microsoft 365 Copilot seats when the value is personal: summarize my email, draft in Word, analyze in Excel, ask Chat about files the user already can open. Agent Builder inside Microsoft 365 is the lightweight path for simple internal agents that stay in the M365 shell. Choose standalone Copilot Studio capacity when you need external channels (public website, custom app, social), multi-agent systems, premium/custom connectors, Dataverse-backed enterprise agents, voice/IVR, computer use, or usage by people without M365 Copilot licenses. Choose classic orchestration (or tightly authored topics under generative mode with narrow tools) when regulated intake, scripted compliance scripts, or branch-by-branch audit trails matter more than free-form conversation.

Cost interaction matters as much as features. M365 Copilot-licensed employees using internal agents under their own identity often incur no additional Copilot Credits for core B2E scenarios, which is why many enterprises start with seat-covered internal assistants. The same agent design serving a public website, partner portal, or unlicensed call-center floor burns the $200/25k pack or PAYG meter. After June 2026, plan new internal agents against the Copilot Studio web app rather than the sunset path for creating classic chatbots in the Teams app. If you only need a fixed approval chain with no conversation, skip Studio and use Power Automate.

What changed in 2026: agentic wave highlights worth building for

Microsoft's 2026 release wave 1 and subsequent monthly Copilot Studio updates moved the product from 'chatbot builder' toward multi-agent, evaluable, governable agent systems. You do not need every preview feature on day one, but you should know which capabilities change architecture decisions so pilots do not paint you into a corner.

Capabilities that are generally available or production-ready enough to plan against: computer use (agents control browsers and desktop apps when APIs are missing); agent-to-agent (A2A) connections so a front-door agent can delegate to specialists; agent evaluations (test sets, multi-turn conversation tests, activity maps) so quality is measurable before scale; the agent usage estimator for credit forecasting; environment-wide analytics across agents (sessions, autonomous runs, task completion trends); model choice for primary orchestration (including generally available ChatGPT-class and Claude options in many regions, with experimental deep-reasoning models); and the Visual Studio Code extension for developer-centric agent ALM.

Capabilities in preview or early production-ready preview that affect roadmap conversations: Work IQ / Microsoft IQ tools to ground agents in real-time Microsoft 365 work context (mail, calendar, files, chats); Foundry IQ connections for knowledge bases tuned in Azure AI Foundry; Entra Agent IDs for per-agent identity and Conditional Access; real-time voice agents for telephony scenarios including Dynamics 365 Contact Center; multi-agent orchestration and skills/memory in the newer agent experience (GitHub Copilot harness path alongside classic); and Windows 365 for Agents MCP server for cloud PC operational control. Treat previews as design options with an exit plan — do not bet month-end close on a preview-only dependency.

For Dynamics 365 SMEs, the practical 2026 stack is still boring by design: generative orchestration with excellent descriptions, Dataverse + SharePoint knowledge under user auth, a short list of well-named tools, classic or tightly constrained paths for money movement, evaluations on a golden test set, and credit math before external publish. Layer computer use only where connectors fail; layer A2A when one agent has become a kitchen-sink monolith; layer voice when phone is a real channel, not a demo.

When to build in Copilot Studio — and when not to

Copilot Studio is the right tool when the included Copilot in your Dynamics 365 apps — or a Microsoft 365 Copilot seat plus Agent Builder — does not cover a scenario and you need a custom agent that knows your data and takes your actions. Strong fits: a customer-facing support agent grounded in your knowledge base and case history; an internal assistant that answers policy and benefits questions from SharePoint; a sales assistant that summarises opportunity context and drafts follow-ups; an operations agent that triages work orders and routes them through an approval flow; a multi-agent system where a front-door agent delegates to domain specialists. In each case the value comes from grounding in your specific Dataverse data and taking actions in your specific systems — neither of which stock app Copilot fully replaces out of the box.

It is the wrong tool when the problem is deterministic rather than conversational. If the requirement is a fixed workflow that runs the same way every time — an approval chain, a scheduled data load, a document generation pipeline — Power Automate is the better surface, and Copilot Studio adds probabilistic orchestration you do not need. It is also the wrong tool for regulated calculations, audit-supporting entries, and anything that must be reproducible to the cent: those should stay in deterministic code or configured business logic, with the agent at most drafting or triaging the surrounding work.

The honest framing is that Copilot Studio is a force multiplier on work you already trust humans to review, and a scoped automation project for anything autonomous. Build the first agent on a low-risk, high-frequency scenario (internal Q&A from SharePoint is the canonical starter), measure the credit consumption, refine the descriptions and knowledge sources against real queries, and only then scale to customer-facing or action-taking agents. SMEs that try to ship a high-stakes autonomous agent as their first build usually hit a governance or budget surprise that a smaller pilot would have surfaced.

How Flectic helps you build agents that ship

Flectic is an AI-driven ERP and CRM implementation partner for SMEs on Microsoft Dynamics 365 and Odoo, delivering remote-first across Canada, the UK, and the US. We are dual-platform and platform-neutral — we implement Business Central, the F&O-tier apps, and Odoo — so we have no incentive to push a build you do not need. For Copilot Studio specifically, we help SMEs pick the right first agent, design the topic graph and descriptions for generative orchestration, wire Dataverse and SharePoint knowledge with correct user authentication, model the credit budget before the agent goes live, and put governance and human-review flows around anything that takes a real action.

Our AI-Accelerated Delivery Framework is designed to deliver up to 3x faster than a conventional rollout — qualified by our delivery methodology, not a blanket guarantee. The most useful thing we can do in a first conversation is run a platform-neutral readiness assessment: name one or two agent scenarios that would actually move the needle for your team, scope the knowledge sources and tools each needs, estimate the monthly credit cost against your real conversation volume, and tell you honestly whether Copilot Studio is the right surface or whether the included Dynamics 365 Copilot — or a deterministic Power Automate flow — already covers it.

Frequently asked questions