Microsoft Copilot Studio Features

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Supports configuration using natural language instructions.

Allows users to customize the tone of agent.

Enables definition of clear security guardrails for agent actions.

Protect APIs from threats using authentication, access control and encryption

Protect sensitive data for digital privacy

Verify the identity of users/devices to enable secure access

Provides support across web, mobile, messaging apps, and other channels.

Allows customization of agent branding, including visual appearance and conversational style.

Equips agents with proactive response capabilities based on predefined triggers.

Facilitates seamless escalation to human employees for complex issues.

Supports various file formats

Processing different types of input like text, voice, images etc.

Provides analytics and reporting on agent performance and interactions.

Offers agents the ability to maintain contextual awareness across interactions.

Ensures compliance with data privacy and governance requirements.

Automates workflows and actions based on agent responses.

Allows the use of APIs for advanced agent configuration.

Enables interoperability with multiple platforms for unified experiences.

Allows integration with CRM data to ground agent responses in business context.

Set up connections to third-party platforms to improve business processes

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Retrieves information from integrated enterprise systems in response to conversational queries

Identifies user intent and maps requests to predefined system actions, queries, or workflows

Initiates tasks, processes, or system workflows based on interpreted user requests

Interprets user requests expressed in natural language to identify intent and relevant entities

Maintains dialogue continuity across multiple messages to support contextual back-and-forth interactions

Enables users to interact with agents through chat, messaging platforms, or voice commands

Maintains contextual information across interactions to support coherent multi-turn conversations

Adjusts responses and available actions based on the user’s identity, role, and system permissions

Connects conversational agents with enterprise systems such as CRM, ERP, ITSM, or collaboration tools

Enforces authentication, authorization, and governance policies for conversational interactions

Track revisions and updates made to files and navigate between different versions

Manage a growing amount of work to meet increased demand without compromising performance

Adjust communications based on previous interactions or personal preferences

Automatically retrieve and pull information from documents, websites, images, data sets, and other sources

Event-driven HTTP callback that automatically sends real-time data between applications

Application programming interface that allows for integration with other systems/databases

Process and analyze human language in text or audio form

Automatically switching to secondary AI model(s) during failure or hallucinations

Assemble applications and processes by dragging over and arranging pre-built components

Ability to interact with various large language models, such as GPT-4, Claude 3, Gemini, Llama 3, and more

Chatbot or AI assistant that answers queries and provides recommendations within an application

Execution of software tests by leveraging automation tools

Collection of processes, policies, and standards to manage the storage & usability of enterprise data

Provides a channel for team members to share media files, communicate, and work together

Pre-defined templates within a library for common cases such as contracts, websites, emails, and more

Designing how agent will communicate, take decisions and behave in different scenarios via flowcharts, scripts, or visual editors

Artificial neural networks using multiple layers of processing are used to extract progressively higher level features from data.

Process of testing an ML algorithm by feeding it training data to learn from

Tools for the systematic analysis of various types of data or statistics

Allow users to access multiple services after entering their login credentials once

Detect and remove errors

Manage the processes involved when making the application ready for use

Managing and resolving errors that occur during program execution to maintain stability

Provides ready-made templates and starter flows for common use cases such as FAQs, lead capture, and support triage.

Allows users to design bot conversation flows using a drag-and-drop, no-code or low-code interface.

Deploys a single built bot across multiple messaging, voice, and device channels without separate rebuilds.

Provides tools to update, patch, and roll back published bots without rebuilding from scratch.

Interprets user intent and extracts entities from free-form text or speech input.

Supports building and running bots that understand and respond in more than one language.

Extends bot logic to telephony or voice channels through speech recognition and speech synthesis.

Tracks bot usage, containment rate, and conversation outcomes through reporting dashboards.

Simulates conversations to validate bot behavior and catch errors before publishing.

Surfaces detailed conversation transcripts and failed-intent logs so developers can diagnose and fix bot behavior.