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Introducing AI Agents: Examples & Use Cases | V7 Go Keynote

V7 • 6:02 minutes • Published 2025-06-26 • YouTube

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📹 Video Information:

Title: Introducing AI Agents: Examples & Use Cases | V7 Go Keynote
Channel: V7
Duration: 06:02
Views: 33,636

Overview

This video introduces the evolution of workplace automation through AI agents, focusing on V7 Go’s vision for delegating repetitive and knowledge-intensive tasks to highly specialized AI agents. The chapters guide viewers from the conceptual shift in software use, through technical explanations and setup, to practical applications and future directions—providing a comprehensive roadmap for leveraging AI agents to maximize business productivity.

Chapter-by-Chapter Deep Dive

Introduction (00:00)

Core Concepts & Main Points:
- The workplace is shifting from using software merely as a tool to actively delegating work to AI agents.
- The increased intelligence of AI models makes it feasible for them to handle tedious and repetitive tasks.
- V7 Go, launched a year ago, is positioned as a generative AI platform to automate knowledge work reliably and at scale.
- V7 Go has already seen adoption across sectors (e.g., asset management, law firms, tax, and small businesses).

Key Insights & Takeaways:
- Automation is no longer futuristic; it’s happening now in various professional contexts.
- The focus is on freeing humans from repetitive tasks to allow for more meaningful work.

Actionable Advice:
- Consider which repetitive tasks in your workflow could be delegated to intelligent agents.

Connection to Overall Theme:
- Sets the stage for understanding why AI agents are important and timely.

AI agents explained (00:50)

Core Concepts & Main Points:
- V7 Go aims to provide a unified interface for automating administrative tasks across companies with high reliability and accuracy.
- Common issues with AI agents in finance, insurance, and legal work include unreliability, complexity, and lack of trust due to errors or missing information.
- V7 Go set three goals for their agents:
1. Agents should know how to solve their assigned tasks before starting.
2. Agents should be configurable by nontechnical users.
3. Agents should accept delegated tasks from anywhere and deliver thorough results.

Key Insights & Takeaways:
- Trust, reliability, and user accessibility are the foundation for successful AI agent adoption.
- The design focus is on making AI agents robust and user-friendly for nontechnical business users.

Actionable Advice:
- Look for AI solutions that prioritize reliability and are easy to configure without deep technical expertise.

Connection to Overall Theme:
- Highlights the challenges in current AI agent adoption and introduces V7 Go’s approach to overcoming them.

Getting started with AI agents (01:38)

Core Concepts & Main Points:
- V7 Go agents are domain experts in specific tasks (e.g., processing tax forms, insurance claims).
- Unlike broad language models (e.g., ChatGPT), these agents are specialists, can follow step-by-step configurations, and adhere to internal guidelines for maximum accuracy.
- Agents can process hundreds of tasks in parallel for consistent output.
- Building an agent involves:
1. Adding sample input files.
2. Defining each step and properties (e.g., extracting figures, making decisions based on company knowledge).

Key Insights & Takeaways:
- Specialization and stepwise configuration lead to higher accuracy and reliability.
- Agents can be tailored to company-specific requirements, ensuring compliance and consistency.

Actionable Advice:
- When deploying AI agents, break down complex tasks into explicit steps and provide relevant input examples.

Connection to Overall Theme:
- Transitions from conceptual benefits to practical steps for implementing AI agents in real business processes.

How do AI agents work? (02:52)

Core Concepts & Main Points:
- Agent configuration is made easier with AI assistance, which can help determine optimal property types, required inputs, and prompt design.
- Agents support flexible input types (single files or bundles) and can handle nonlinear workflows (e.g., research, subtables).
- Users can track parallel task progress via the agent table.
- Launching agents is streamlined with templates—either default or team-created.
- The case-based UI allows for managing tasks, launching agents, and collaboration.
- The AI Concierge: a meta-agent that delegates tasks to the correct specialist agent and provides summarized results, acting as a “chief of staff.”

Key Insights & Takeaways:
- Automation extends beyond simple tasks to complex, multi-step processes.
- The agent network can scale across many specialized agents, coordinated by a central Concierge, enhancing efficiency.

Actionable Advice:
- Use pre-built templates for faster deployment, and utilize the Concierge to manage multi-agent workflows.

Connection to Overall Theme:
- Deepens the technical understanding of how V7 Go agents operate and how they integrate into team workflows.

Use case deep dive (04:16)

Core Concepts & Main Points:
- Example: Submitting an NDA to Go triggers the Concierge, which delegates to an NDA review agent configured with team guidelines.
- Results are shown in real time, with each property cited directly to the source document for transparency and auditability.
- The Concierge integrates with various platforms (email, Slack, Teams), making agent access seamless.

Key Insights & Takeaways:
- Real-world application demonstrates how agents reduce manual review and provide traceable, reliable outputs.
- Multi-platform integration increases adoption and usability.

Actionable Advice:
- Integrate AI agent workflows with existing communication and document management tools for maximum impact.

Connection to Overall Theme:
- Bridges abstract explanations with tangible business value and user experience.

Conclusion (04:55)

Core Concepts & Main Points:
- Over the past 10 weeks, V7 Go customers have automated tasks like lease abstraction, investment analysis, and insurance claims.
- AI in the workplace is still in early stages; rapid iteration is ongoing with new features released frequently.
- V7 is hiring and encourages viewers interested in shaping the future of human-computer interaction to join.
- The team is committed to thoroughly solving key use cases and surpassing human-level accuracy.
- Goal: Free users from repetitive tasks to focus on high-value business activities.

Key Insights & Takeaways:
- Early adopters are already seeing significant productivity gains.
- The company is committed to driving innovation and solving real business problems.

Actionable Advice:
- Consider piloting AI agent solutions for your business processes, especially if your needs align with those discussed.

Connection to Overall Theme:
- Reinforces the transformative potential of AI agents and encourages ongoing engagement and exploration.

Cross-Chapter Synthesis

Recurring Themes & Concepts:
- Delegation & Automation: The shift from manual work to automated delegation via AI agents is the central narrative (Introduction, AI agents explained, Conclusion).
- Specialization & Reliability: Agents are designed to be specialists, delivering reliable, consistent results (Getting started, AI agents explained).
- Accessibility & User Experience: Emphasis on nontechnical user configuration and seamless integration into workflows (AI agents explained, How do AI agents work?, Use case deep dive).
- Transparency & Trust: Outputs are traceable and grounded in source documents to build trust (Use case deep dive).
- Scalability & Collaboration: From parallel task processing to networked agents coordinated by the Concierge (How do AI agents work?, Use case deep dive).

Learning Journey:
- Begins with the “why” (the need for automating repetitive work), moves to the “what” (what AI agents are and what sets them apart), then to the “how” (building, configuring, and launching agents), and finally to the “so what” (real-world impact and future directions).

Most Important Points Across Chapters:
- Delegating repetitive, knowledge-based tasks to specialized AI agents increases productivity (Introduction, Conclusion).
- V7 Go agents are designed for high reliability, configurability, and human-level (or better) accuracy (AI agents explained, Getting started).
- The Concierge enables efficient management of multiple agents and tasks, providing summarized and actionable outputs (How do AI agents work?, Use case deep dive).
- Real-world use cases illustrate immediate business value and transparency (Use case deep dive).
- The platform is rapidly evolving, with ongoing improvements and opportunities for user involvement (Conclusion).

Actionable Strategies by Chapter

Introduction (00:00)
- Identify and target repetitive, tedious tasks in your organization for potential automation with AI agents.

AI agents explained (00:50)
- Seek AI platforms that are reliable, easy to configure, and accessible to nontechnical users.
- Ensure agents are thoroughly trained and able to handle tasks before deployment.

Getting started with AI agents (01:38)
- Construct agents by providing sample inputs and defining explicit, stepwise processes.
- Tailor agents to follow your internal company guidelines for accuracy and compliance.

How do AI agents work? (02:52)
- Leverage AI-assisted configuration to optimize agent setup.
- Utilize templates and case-based UIs for efficient task management.
- Use the Concierge for orchestrating complex, multi-agent workflows.

Use case deep dive (04:16)
- Integrate agent workflows with existing team communication tools (Slack, Teams, email).
- Ensure outputs are traceable to original sources for transparency and auditability.

Conclusion (04:55)
- Pilot automation in areas with high manual workload; iterate and expand as new features are released.
- Stay engaged with platform updates and consider contributing feedback for feature development.

Warnings or Pitfalls

  • Unreliability of Generic Agents: Historical challenges with AI agents include unreliability and lack of transparency (AI agents explained).
  • Complexity for Nontechnical Users: Importance of ensuring that agents are easy to configure and use (AI agents explained).

Resources, Tools, or Next Steps

  • V7 Go Platform: Use pre-built templates or create custom agents for specific business processes (How do AI agents work?).
  • AI Concierge: Use this feature to coordinate agent workflows and manage tasks efficiently (How do AI agents work?, Use case deep dive).
  • Multi-Platform Integration: Access agents via Slack, Teams, email, etc., for seamless workflow integration (Use case deep dive).
  • Hiring & Community: Opportunities to join V7 and contribute to the evolution of AI-driven work (Conclusion).

Chapter structure for reference:
- Introduction (starts at 00:00)
- AI agents explained (starts at 00:50)
- Getting started with AI agents (starts at 01:38)
- How do AI agents work? (starts at 02:52)
- Use case deep dive (starts at 04:16)
- Conclusion (starts at 04:55)

📝 Transcript Chapters (6 chapters):

📝 Transcript (153 entries):

## Introduction [00:00] [00:11] [Music] [00:19] We have begun a transition from working [00:20] with software as a tool to delegating [00:23] our work to software in the form of AI [00:25] agents. As models get smarter, it's the right time for them to take over any work that is tedious and repetitive. And today, we're unveiling our vision for Go agents. One year ago, we announced V7 Go, a generative AI platform to automate any form of knowledge work reliably and at scale. Go started a wave of adoption [00:44] from asset managers to law firms, tax [00:46] giants, and small businesses who now [00:48] delegate all of their repetitive work to ## AI agents explained [00:50] [00:50] AI. Our goal was to build a single user interface that could tackle all administrative work across your company and do so very reliably. No hallucinations, easy to build with, and reaching human level accuracy. This year, we really wanted to nail the experience of using agents within Go. AI [01:07] agents across finance, insurance, and [01:09] legal work can be fickle. They're unreliable and complex tasks. They sometimes take nonsensical paths, and they get lost, missing out on crucial information. and overall they're pretty hard to trust with work that we're already good at doing. We've been hard [01:22] at work to solve this and set aside [01:24] three goals. Agents should know how to solve the work that they're assigned to before they begin. They should be configurable by nontechnical users and they should be delegated tasks from anywhere, taking their time to solve them thoroughly before bringing us final results. So today we're happy to ## Getting started with AI agents [01:38] [01:39] announce Go agents. Body free up free body. [Music] [Music] Agents on V7 Go are experts in individual tasks like processing a tax form, an information memorandum, or an insurance claim. Rather than figuring out how to solve something from a single prompt, they can be configured step by step, maximizing their accuracy, and follow any of your internal guidelines. [02:29] Unlike ChachiPT, an agent is a [02:31] specialist in a single domain and can [02:33] run hundreds of tasks in parallel, [02:35] giving you consistent results each time. Here's how you build one. First, you add some sample input files and then you define each step in further properties from extracting specific figures to reasoning through a decision based on your company knowledge like evaluating a deal. Prompting has also become far ## How do AI agents work? [02:52] [02:54] easier. You can now use AI to configure any of these steps, letting it decide the best property type to use, what input it needs, and the right prompt itself. Following today's best practices, agent inputs can be a single file or a bundle like a small data room. And they can take nonlinear paths such as performing further research on an asset, developing subtables, and dedicated steps. And you can keep adding [03:17] files to your agent table where you'll [03:18] see all of your tasks being performed in [03:20] parallel. When you're ready to go, launching agents to your team is very easy. The homepage of Go lets you pick any of the templates available by default, plus any of the ones created by your team. Here I'm picking a SIM review agent to triage the 70page investment deck full of financial information. When [03:36] an agent is launched, it runs on a split [03:38] screen. This new UI is called a case. It represents all of the work that I need to do on this investment opportunity. It can be shared and I can use it to launch multiple agents on the data I'm working with or ask follow-up questions. [03:51] There will be times I don't know exactly [03:52] which agent can help me with my next [03:54] query. Some teams have already produced dozens. So for this we developed an additional agent that sits in the chat view and delegates commands to your agent network. We call this AI concierge. If agents are specialists, [04:08] the concierge is their chief of staff. It delegates work to the right agent and returns a summary of its results without needing to sift through all of their ## Use case deep dive [04:16] [04:16] reasoning. Say I receive an NDA. I can just throw it into Go and Concierge will delegate it to an NDA review agent that my team has previously configured with my guidelines. It will start running and show me the results in real time. Each [04:29] property is grounded to the source [04:31] document with citation that takes me [04:33] exactly to the spot in the document [04:35] where that information was sourced from. Concierge has all the capabilities of a consumer model like chatt and it can also launch specialist agents and answer any follow-up questions related to the table of agent results. It sits at the homepage of Go and it can be reached by email, Slack, Teams or any integration. ## Conclusion [04:55] [04:56] Over the last 10 weeks, customers on B7 [04:58] Go have built agents to automate lease [04:59] abstraction, analyzing investments, [05:02] processing insurance claims, and dozens [05:04] of more business processes that were [05:05] previously done manually. This is very early days for AI in the workplace, and like many other startups, we're shipping fast with new functionalities coming every week. You can expect more from us in a couple of weeks time. V7 is hiring across all positions and if you're excited about defining a new way to interact with computers that feels more human and lets us achieve 100 times more, come talk to us. We're one year [05:27] into this journey with V7 Go, and [05:29] there's a lot ahead of us. We realize it's hard to pick the right vendor for AI across hundreds of upstarts. Because of this, we're especially keen in thoroughly solving the use cases below, going beyond average human level accuracy. If any of these is important to you, V7 is something you should try. [05:46] We're excited about what you'll create [05:48] next and how it will unlock human [05:49] potential so you can be free of [05:51] repetitive tasks and focus on what [05:53] matters for your business. I hope to see you soon on the platform.