Knowledge work automation
17 min read
—
Oct 6, 2025
Looking for a practical path to build powerful AI agents? This guide contrasts complex code-heavy methods with the accessible, no-code approach of V7 Go.
Imogen Jones
Content Writer
The air is thick with talk of agentic AI. It's fair to say that many businesses are suffering from a severe case of FOMO; 75% of surveyed senior executives believe AI agents will transform workplaces "more than the internet did", but 46% are concerned that their companies may fall behind competitors in adopting AI agents.
The challenge is execution.
For most businesses, the idea of creating an AI agent seems abstract, complex, and locked behind a wall of Python code. While developers wrestle with intricate frameworks and business users wait for IT support, a new approach is emerging that makes AI agents accessible to the people who understand business processes best.
This guide will walk you through a practical, step-by-step approach to building AI agents.
You’ll see how traditional, code-heavy methods compare to emerging no-code options, and how a platform like V7 Go makes it possible to design agents that actually work.
In this blog:
What an AI agent is (and isn’t)
Different options for building agents
A simple, no-code path to creating agents
Key use cases across industries
How to get started today
What is an AI Agent?
An AI agent is software designed to perceive its environment, make decisions, and take actions to achieve specific goals, all without constant human direction. Unlike traditional automation, which follows rigid if-then rules, AI agents can adapt to new situations by applying reasoning and contextual understanding.
If that sounds complex, it’s partly because the term has been blurred. Many tools today engage in “agent washing” by branding basic automation as AI agents, even though they lack true autonomy, reasoning, or decision-making.
To put it in the simplest terms; think of agents as your assistant. They can answer your questions, and perform actions on your behalf.
The Core Components of an Agentic System:
Planning & Reasoning: This is the agent’s core logic. It allows the system to decompose a high-level goal (e.g., “Analyze this vendor contract for risks”) into a series of smaller, executable steps. This involves multi-step reasoning, where the outcome of one step informs the next, allowing the agent to navigate complex problems dynamically, much like a human project manager.
Tool Use: Agents are not all-knowing; they are expert tool users. A tool can be a web search API, a connection to a CRM like Salesforce, a Python code interpreter for calculations, or even another specialized AI agent. The agent's intelligence lies in knowing which tool to use, when to use it, and how to interpret the results to move closer to its goal.
Memory: To perform multi-step tasks effectively, an agent needs memory. This includes short-term memory (to remember the current conversation and task context) and long-term memory (to learn from past interactions and improve future performance). This ability to learn and adapt is a key differentiator from static automation scripts that repeat the same mistakes indefinitely.
For a deeper dive, explore our complete guide to AI agents.
What AI agents are not:
Sophisticated chatbots with better responses: While agents can have conversational interfaces, their defining trait is action and autonomy, not just witty conversation.
Traditional RPA bots following predetermined scripts: Robotic Process Automation is deterministic and follows a rigid, pre-coded path. An AI agent is dynamic, choosing its own path based on its goal and real-time data.
Simple workflow automation tools: Basic workflow tools connect apps in a linear sequence. An agent can create its own sequence, loop back, and make decisions along the way.
Magic solutions that work without proper setup: Agents require clear goals, access to the right tools, and well-defined business logic to be effective. They don't operate in a vacuum.
The Traditional Path to Building AI Agents
For years, building an AI agent presented a binary choice between powerful but overly complex frameworks, or easy-to-use tools that couldn’t do much.
Neither option truly served the business users who stood to gain the most, leaving them dependent on specialized engineering talent and facing high maintenance overheads.
Building Agents in LangChain
LangChain is the default starting point for many developers venturing into agentic AI, and for good reason. It’s open-source, flexible, and empowers developers to build intelligent AI agents with rich context management and dynamic decision-making capabilities.
However, while LangChain shines as a prototyping toolkit, it can be less suitable when the goal is to give everyday business users practical, stable AI agents to improve their workflows.
"Unnecessary complexity due to over abstraction which in turn impacts maintainability, customization and productivity," reports one frustrated developer. This sentiment captures the core problem. LangChain is a powerful prototyping tool, but it can create a web of abstractions that become a liability in production.
It’s not uncommon for teams to spend weeks building demos with LangChain, only to replace it when preparing for production deployment. The reasons are consistent: dependency sprawl, frequent breaking changes, and documentation gaps that make debugging harder than it should be. For enterprise environments where stability and predictability are essential, this volatility becomes a major challenge.
In short, LangChain is an excellent place to start, but it isn’t always the best place to stay when building durable, practical AI agents.
Building agents in AutoGen
Microsoft’s AutoGen is another powerful framework, particularly strong at orchestrating multiple agents that collaborate on tasks. It offers a structured approach to building multi-agent systems and it's been embraced by technically advanced teams for its flexibility.
That said, AutoGen is very much a tool built by engineers, for engineers. To use it effectively requires not only deep Python programming expertise, but also a working knowledge of concepts like inter-agent communication, task delegation, and conflict resolution, knowledge that most business users simply don’t have.
Microsoft has introduced AutoGen Studio, a visual interface intended to reduce this barrier. But while it lowers the entry point somewhat, it still carries significant limitations. At present, Studio supports only two-agent workflows and offers a narrow set of serializable properties, which is likely far too restrictive for most working environments.
Exploring "No-Code"
At the other end of the spectrum are the so-called “no-code” platforms such as Zapier AI and Microsoft Power Platform. They promise simplicity, but often introduce a different kind of complexity.
While these platforms remove the need to write code, they still demand a highly technical mindset. Non-technical users quickly find themselves grappling with tasks like configuring API endpoints, mapping data fields between applications, interpreting JSON schemas, or designing conditional logic. These concepts may feel natural to engineers, but they can be overwhelming to business professionals.
This creates a persistent skills gap. Business experts who intimately understand the nuances of a process—the exceptions, the edge cases, the real-world context—can't build the agents they need. Meanwhile, the developers who can build them often lack that deep business context, resulting in brittle solutions that fail when they encounter the messiness of reality.
Too often, this dynamic results in stalled projects and underwhelming outcomes. This is not because the tools are bad, but because they fail to bridge the divide between business knowledge and technical execution.
A New Paradigm: Creating AI Agents with V7 Go
V7 Go represents a fundamentally different approach to AI agent creation, designed for flexible, powerful document-aware automation. It acts as an intelligent document hub, orchestrating complex and scalable workflows through an intuitive interface that bridges the skills gap left by traditional tools.
In V7 Go, agents are constructed from three key components—properties, prompts, and tools—that work together to define both their identity and their capabilities.
Properties capture the essentials: the agent’s name, purpose, input formats, and the type of output it should produce. Prompts provide the reasoning layer, giving the agent clear instructions, examples, and decision rules so it knows how to interpret information or respond to tasks. Tools can include LLMs for natural language understanding, Python functions for custom logic or calculations, API connectors to bring in data or trigger external actions, and more.
Together, these elements let you create highly specialized agents that combine domain knowledge, structured logic, and real-world capabilities, without complex code.
“AI Foundation models are now smart enough to take care of a lot of our least-favorite tasks at work, and much of the data they need to complete this is readily available in the cloud. There is a new market for AI workflows that enable foundation models to learn from this company data, making them more reliable at scale with every completed task. This is a leap far past the one-to-one interactions with chatbots, towards something that takes care of repetitive work on autopilot whenever it reappears.”
— Alberto Rizzoli, CEO and Co-Founder, V7 Labs
Here are five reasons V7 Go makes it easier than ever to build your first AI agent.
A single, monolithic AI call might fail on nuanced inputs, and some approaches that force users into rigid, predefined paths.
V7 Go utilizes a system of data inputs and outputs that are configured to simulate the chain of thought process, breaking tasks into sequential, logical reasoning steps. This is designed to mimick how a human analyst approaches a complex problem.
At the core of V7 Go is Index Knowledge, a proprietary technology that transforms large, information-dense files into small, searchable indexes. The key difference is that chat AI products simply inject text from documents into a prompt, whilst Index Knowledge utilizes the model itself to develop a data extraction plan, again much like a human analyst would.
This allows LLMs to query information more accurately than standard retrieval augmented generation (RAG), with greater precision at the cost of additional compute.You can see this illustrated below.
Learn more about Index Knowledge here
3. Visual Grounding
V7 Go also includes AI Citations through Visual Grounding, which allows users to verify exactly where in a document or image the model has sourced its information. This increases explainability and trust in AI-driven decisions.
For example, in an invoice-processing use case with GPT-5, V7 Go highlights the precise text fragment used for a decision, rather than pointing vaguely to a page or paragraph. This fine-grained highlighting helps to accelerate and promote human-in-the-loop review.
V7 Go's visual grounding provides full transparency by showing exactly where in a document an insight was sourced from.
V7 Go comes equipped with a library of specialized agents across six industry verticals, each designed for specific, high-value business functions. You can gradually adapt these to suit your needs, or create your own from scratch to fulfil your most bespoke workflow requirements.
Finance agents handle financial due diligence, statement analysis, investment analysis, and data room processing.
Legal agents manage contract review, regulatory compliance, and legal research.
Insurance agents automate claims processing, coverage analysis, and risk assessment.
Real estate agents that handle the heavy lifting of property paperwork and analysis.
These aren't generic chatbots with industry-specific prompts. Each agent is a sophisticated system that understands domain-specific document structures, terminology, and business logic, designed to meet the typical requirements of your industry.
V7 Go's interface is designed to resemble a "smart spreadsheet," an environment business users are already comfortable with. Users describe what they want in plain English, and the system translates that goal into a visual workflow complete with conditional logic, human-in-the-loop checkpoints, and robust error handling.
The platform manages all the technical complexity in the background, like API integrations, data transformations, and model management, while presenting business users with intuitive controls for defining and refining their processes.
Practical Walkthrough: Building a Contract Review Agent in V7 Go
Let's walk through a very simple, practical example: creating a supply contract review agent.
Step 1: Add a Name and Description
When you go to add a new agent in V7 Go, you'll see a list of templates you can select. Looking for a CIM Due Diligence agent, Slip Ingestion, Automated Invoice Processing? With just a click, you can have the basics already set up for you, then make any further changes you need.
If you want to start from scratch for something more bespoke, that's easy, too. Just add a descriptive title and brief explanation of what your agent will do.
This helps Concierge, V7 Go's powerful chat functionality, identify and use your new agent whenever you or your team need it.
Step 2: Configure Your Properties
The File property is a natural place to start, because Go can extract information out of these files, and use them to create structured information that can be used downstream. In our case, we could re-title this as Supply Contracts.
When you're ready to add files, you can upload the contracts from your computer, pick from your Library, connect a Knowledge Hub, or just drag-and-drop.
Next, we can use a Text property, which is extremely flexible. In the example below, we've retitled it to "Supply Quantities", specified that "Supply Contracts" is the input, and written a simple natural-language prompt instructing AI to extract all quantities and due dates from the uploaded supply contracts.
Step 3: Refine Your Model
Text and files are just the start; add any other properties you need, such as;
Single Select & Multi Select (structured, pre-defined choices)
Number property (capturing and analyzing numerical data)
URL Property (capture, validate, and leverage website links)
Reference Property (connect related data across agents)
Collection Property (extract and analyze structured tabular data)
JSON Property (store and process structured data in a machine-readable format)
Page Splitter Property (split large documents into smaller files for further analysis)
Python Tool (automate and customise workflows)
For example, in our Supply Contract Agent, we might want a single-select property that gives a green flag when no shipments are due within the next 10 days for a particular customer, a orange flag if there is an upcoming shipment, and a red flag if a shipment is already overdue. We could also add a number property calculating the total weight of each shipment.
You can set up any integrations you need through Zapier, a no-code solution to building integrations that rely on triggers and actions to create automations. Learn more here.
Step 4: Use Your Agent!
You're ready to go! Use your AI agent to streamline and enhance your workflows. It's easy to update and refine your agent over time, so that it best meets your needs.
Looking for another AI agent example? In this video, Mike from V7 walks you through a simple data extraction agent.
You can also learn more in our article: Creating Your First Agent. We also have a full playlist here with more information, from advanced AI prompting to RAG.
The Tangible Business Impact: Use Cases Across Industries
Properly implemented AI agents can delivering measurable results across every business function. Below is just a small selection of use cases, and how AI agents will be used to create greater efficiency over the coming years.
AI For Legal Teams
Famously (or infamously) document heavy, legal practice is one of the most exciting use cases for AI agents. These agents can slash contract review times from hours to minutes, quickly analyzing terms and flagging risks that once required painstaking manual effort.
Beyond contracts, they help legal teams surface relevant case law, track regulatory changes, and automate routine filings, cutting through the administrative grind. The result: lawyers are freed to focus on strategy and high-value tasks instead of drowning in paperwork.
AI for Finance
Few departments can feel the impact of AI agents as sharply as finance. Take invoice processing: what previously required 10 hours per accounts-payable batch can now be finished in just a couple of minutes thanks to automated data extraction, approval routing, and ERP integration.
The benefits extend to more complex financial workflows as well. Financial services firms use AI agents to analyze Confidential Information Memorandums, due-diligence packs, and multi-document data rooms, as well extracting data from 10-K reports.
These agents deliver significant accuracy improvements and dramatically faster turnaround times, so finance teams can focus on analysis and strategic decision-making instead of tedious manual review.
AI for Operations and Supply Chain Intelligence
Operations and supply chain functions thrive on speed, precision, and foresight, qualities that AI agents are uniquely positioned to deliver. According to IBM research, 64% of supply chain executives say AI is already transforming their supply chain operations workflows.
Agents can be used to monitor equipment for predictive maintenance, analyze suppier contracts, manage stock levels, respond to customer service inquiries and more. The result is a supply chain that is more efficient, resilient, and adaptive.
See example workflows in V7 Go below.
AI for Human Resources
In HR, AI agents are reducing the administrative load while enabling a more strategic, data-driven approach to talent management.
Over the next few years, their impact will be twofold:
In the coming years, the use of AI will impact HR departments in two significant ways: First, by streamlining HR operations and leveraging workplace data to improve the talent planning and management process. Secondarily, AI will require HR departments to foster a change-minded culture capable of embracing new ways of working. This shift requires some realignment, but the potential benefits are immense.
— Molly Hayes, IBM
This shift requires realignment, but the potential rewards are significant: faster hiring cycles, more accurate operations, and workplaces that can adapt to technological change with confidence.
Measuring Real ROI
The return on investment is clear and compelling. 74% of organizations report AI investments meeting or exceeding expectations, with 20% seeing ROI above 30% for their most advanced initiatives.
Our findings revealed companies allocating 5% or more of their budget to AI are gaining vastly more ROI compared with those spending less.
— EY AI Pulse Survey
Successful programs follow a disciplined path that favors strategy over hype. Defining a clear objective early, mapping it to business priorities, and setting measurable outcomes dramatically increase the chances of early-stage ROI.
Those quick wins, in turn, help unlock budget and confidence for more ambitious projects.
Learn more in our blog How to Secure the Best ROI from Your AI Investment
The Future is Agentic: What’s Next for Business Automation?
Industry analysts agree that AI agents are set to redefine how businesses operate, but they also caution that execution will determine who thrives and who stumbles.
Here’s what to expect as this technology matures.
Salesforce CEO Marc Benioff envisions a future “digital workforce,” where humans and AI agents collaborate seamlessly to handle increasingly complex knowledge work. The market for this kind of digital labor could reach trillions of dollars as adoption accelerates.
Gartner predicts that by 2028, one-third of enterprise software applications will include agentic AI, up from less than 1% today. They also expect 15% of everyday business decisions to be made autonomously by AI agents.
But scale brings risk. Gartner warns that over 40% of agentic AI projects may be canceled by 2027 due to rising costs, unclear business value, or weak risk controls. Success will depend on starting with measurable processes, selecting the right vendors, and managing organizational change from day one.
The rise of accessible AI tools is accelerating democratization, and the barrier to entry for creating agents and other sophisticated applications is crumbling. As Jody Bailey, Stack Overflow's CTO, notes: "Generative AI will democratize coding and grow the developer community by several folds."
This shift empowers business analysts to participate fully in AI development, creating solutions that solve real-world problems without deep technical expertise.
The future of AI in the enterprise is about orchestrating systems more intelligently. Successful AI agents will function as the central brain, coordinating a digital workforce of RPA bots, APIs, and software tools.
This “intelligent automation” blends the speed of rule-based processes with the flexibility of cognitive reasoning, enabling organizations to scale efficiency while adapting to change in real time.
Stop Talking About AI Agents and Start Building Them
The window between AI agent hype and practical implementation is closing rapidly. Organizations that act now, while the technology is still emerging, will establish competitive advantages that become harder to replicate as the market matures.
The path forward requires three clear commitments:
1. Start with Clarity, Not Complexity. Identify specific business processes that consume significant time and follow predictable, document-heavy patterns. Document review, invoice processing, and customer inquiry routing are excellent starting points because success is easily measurable.
As one experienced builder on Reddit advised, "Build something stupidly simple first. My first 'agent' literally just monitored my email for receipts and added them to a Google Sheet. Took 3 hours, felt like magic. Don't try to build Jarvis on day one."
2. Choose Tools Designed for Business Users. The future belongs to platforms like V7 Go that prioritize user experience and abstract away technical complexity. This is what will determine which organizations successfully deploy AI agents at scale, avoiding the common pitfall where a project gets stuck in IT for months.
3. Plan for Orchestration, Not Replacement. The most successful AI agent implementations enhance human decision-making, they don't eliminate it. Design workflows with appropriate human-in-the-loop checkpoints, especially for high-stakes decisions where expert oversight is critical.
The question isn't whether your organization will use AI agents, but whether you'll build them on your timeline or scramble to catch up as competitors pull ahead. The tools exist, and the business case is proven.
If you're ready to see how agents could transform your workflows, book a chat with our expert team
