AI Co-Pilot vs Agentic AI – Key Differences

If you have been paying attention to the AI space, you have undoubtedly heard about AI Co-Pilots. AI Copilot is a tool that assists users and makes their work easier by injecting intelligence into their workflow.

However, more recently, AI Agents or Agentic AI have arrived as the new kid on the block who seems to be drawing all the limelight (and for good reason!).

In this blog, we will overview each and compare the two AI technologies. We will also try to understand why Agentic AI is on track to replace Co-pilot as the dominant form of AI in enterprise applications.

Let us look at:

1. Critical Capabilities of AI Co-Pilot vs Agentic AI
2. Use case and examples of each
3. Business Outcomes and ROI Possibilities
4. Differences in the technology stack
5. Implementation approach and how to get started

Key capabilities of AI Co-Pilot vs Agentic AI

AI Copilot: Assisting and Augmenting

An AI copilot is an augmentation tool designed to work alongside humans, assisting and enhancing their abilities in various tasks. It can save humans time by processing information quickly, offering real-time recommendations, generating content, and automating routine tasks. However, they always operate under direct human guidance. The copilot enhances productivity by increasing the efficiency of processes such as writing, coding, or managing data, but it does not act independently. The copilot’s strength lies in its ability to elevate human efficiency without stepping beyond the boundaries of human oversight.

Agentic AI: Autonomous Decision-Making

In contrast, agentic AI represents a more advanced form of artificial intelligence that operates autonomously, making independent decisions and executing actions without constant human input. These systems are designed to mimic human-like reasoning and problem-solving, allowing them to act independently to accomplish predefined business goals. Agentic AI can learn from its environment, refine its strategies, and take actions without human intervention, often functioning in complex and dynamic environments.

Use case and examples

Here are some examples of AI Co-Pilots that enhance the users' efficiency in a range of areas, from sales to content writing

• Microsoft Copilot: Assist users with MS Office products like Word or Excel
• Jasper AI: Helps create new content using AI
• GitHub Copilot – Helps developers create code
• Salesforce Einstein: Helps sales teams become more efficient by automating sales workflows and data
• Adobe Firefly: Helps users create creative content across Adobe products

Here are some examples of agentic AI that are more autonomous and drive business outcomes independently.

• Waymo self-driving cars:  Cars that can independently drive to a destination without assistance from a driver
• Salesforce Agent force: Drives customer support across multiple channels
• Piper AI SDR – Generates Sales Leads autonomously
• Rezolve.ai – Agentic AI for ITSM that automates Level 1 IT & HR support

Business Outcomes and ROI of AI Co-Pilots & Agentic AIs

In general, Copilots typically offer a 5-10% improvement in employee productivity by automating steps in the workflow and helping drive insights into a larger volume of data.

By being autonomous, Agentic AI can operate at 100% efficiency (if a task can run with no human input). Most commercial Agentic AI systems already deliver 20-50% efficiency improvements.

Differences in the technology stack

Co-pilots typically function by adding a generative AI or similar AI capability to an existing product to make it more efficient. For example, some legacy ITSM systems have added a Ticket Summarization feature using generative AI.

So, the two core pieces of a Co-Pilot are:

• An existing application (like an ITSM or MS Office) in which to add a copilot
• A GenAI LLM models like OpenAI GPT 4o or Anthropic Claude that can add AI capabilities to the legacy application

Agentic AI relies on a more complex architecture to deliver fully autonomous outcomes. In a SaaS-based AI platform, the typical components are:

• RAG Architecture to connect to relevant enterprise content
• Data Integration to unstructured and structured data
• API integrations for workflow automation
• Memory – maintaining context for AI
• Agents that supervise and manage work and orchestrate it all

Frameworks like Lang Chain are examples of platforms supporting some of the above capabilities.

Implementation approach and how to get started

To get started with copilots, the easiest path may be to simply enable them in products like MS Office or Salesforce. However, for all the reasons discussed earlier, copilots may already be on the way out to make way for Agentic AI.

To get started with Agentic AI, you can start with two possible paths:

• Build using a platform like Lang Chain – this can be complex and expensive.
• Leverage a purpose-built platform: To automate IT Support, for example, get started with a purpose-built platform like Rezolve.ai.

See how Rezolve.ai can pave way for higher ROI and operational excellence for your IT support

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