There's a revolution happening in artificial intelligence right now, and it's not about chatbots. It's about AI agents: autonomous systems that can reason, plan, use tools, and execute multi-step workflows without human intervention. Agentic AI is already transforming how enterprises handle everything from customer support to supply chain logistics. But there's a massive problem: building these agents requires deep technical expertise that 99% of businesses don't have.
At Encode Digital, we've spent the last several months working on something we believe will change that. We're building a no-code visual builder that lets businesses design, deploy, and manage AI agents without writing a single line of code. This isn't a side project or an experiment. It's the product we're most excited about in our company's history, and we want to share why.
What Is Agentic AI and Why Should You Care?
If you've used ChatGPT, you've interacted with a conversational AI. You ask a question, it responds. That's useful, but it's fundamentally reactive. Agentic AI is different. An AI agent can take a high-level goal, like 'process this insurance claim' or 'qualify this sales lead and schedule a demo', and autonomously figure out the steps needed to accomplish it. It can browse databases, call APIs, send emails, update CRMs, make decisions based on business rules, and loop back when something doesn't work.
This isn't theoretical. Companies like Klarna have replaced hundreds of customer service roles with AI agents. Salesforce has built an entire product line around autonomous agents. Every major cloud provider is racing to offer agentic AI infrastructure. McKinsey estimates that agentic AI could automate up to 30% of knowledge work tasks by 2028.
Agentic AI isn't the next iteration of chatbots. It's an entirely new category of software: autonomous digital workers that can own entire business processes end to end.
The Problem: A Massive Accessibility Gap
Here's what we kept seeing with our clients and in the broader market. Business leaders understand the potential of AI agents. They can see the use cases clearly: automate lead qualification, handle tier 1 support, process invoices, onboard new customers, generate reports. The vision isn't the problem.
The problem is implementation. Building an AI agent today requires prompt engineering expertise, knowledge of LLM orchestration frameworks like LangChain or CrewAI, the ability to write custom tool integrations, infrastructure for hosting, monitoring, and scaling agents, and ongoing maintenance as models and APIs evolve. That's a full engineering team. Most mid market B2B companies, the ones that would benefit the most from AI agents, don't have that capability in house. And hiring an agency for every agent workflow at $15,000 to $50,000 per build doesn't scale.
Our Solution: A Visual Builder for AI Agents
We're building a platform where you design AI agents the same way you'd build a flowchart. Drag, drop, connect, configure. No code required.
How It Works
The visual builder is centred around a canvas where you construct agent workflows using nodes and connections. Each node represents a capability: an LLM reasoning step, a tool call, a decision branch, a human in the loop checkpoint, or an integration with an external system. You connect these nodes to define the flow of logic, and the platform handles everything underneath: orchestration, error handling, retries, logging, and deployment.
- 1.Define your agent's goal and trigger (e.g., 'When a new support ticket arrives...')
- 2.Drag in reasoning nodes to give your agent decision making capability
- 3.Connect tool nodes for actions: send email, query database, update CRM, call API
- 4.Add conditional branches for different scenarios and edge cases
- 5.Set human in the loop checkpoints where approval is needed before proceeding
- 6.Test the agent with real or simulated data directly in the builder
- 7.Deploy with one click. The platform handles hosting, scaling, and monitoring
Built for B2B From Day One
This isn't a toy for hobbyists. We're designing specifically for B2B operations teams, revenue teams, and business process owners. That means enterprise grade security and data handling, role based access control and audit logs, native integrations with the tools B2B companies already use (HubSpot, Salesforce, Slack, Xero, Google Workspace, and more), human in the loop workflows so agents escalate to humans when confidence is low, and detailed analytics showing exactly what each agent is doing, how it's performing, and where it's getting stuck.
Real Use Cases We're Building For
These aren't hypothetical. These are the workflows our early design partners are asking for.
Inbound Lead Qualification
A new lead fills out a contact form. The agent researches the company (LinkedIn, website, Crunchbase), enriches the CRM record, scores the lead against your ICP criteria, drafts a personalised follow up email, and books a meeting with the right sales rep. All within minutes of the form submission. No human touched it. No lead fell through the cracks.
Customer Support Triage and Resolution
A support ticket comes in. The agent classifies the issue, checks the customer's account status and history, searches your knowledge base for a resolution, and either resolves it automatically or routes it to the right specialist with full context attached. First response time goes from hours to seconds.
Invoice Processing and Accounts Payable
An invoice arrives via email. The agent extracts the data, matches it against purchase orders, flags discrepancies, routes approvals to the right manager based on amount thresholds, and creates the entry in your accounting system. What used to take a finance team member 20 minutes per invoice happens in under a minute.
Employee Onboarding Workflows
A new hire starts Monday. The agent provisions their email, sends welcome messages on Slack, schedules orientation meetings, assigns onboarding tasks in your project management tool, and follows up on incomplete items, adapting the workflow based on department, role, and location.
Why Now? The Convergence That Makes This Possible
We couldn't have built this two years ago. Several things had to converge.
- LLM reasoning capabilities have reached a threshold where agents can reliably handle complex, multi step tasks with acceptable error rates
- Tool use and function calling APIs are now standardised across major model providers (OpenAI, Anthropic, Google), making it practical to build model agnostic agent platforms
- The cost of LLM inference has dropped dramatically. Tasks that cost $1 in 2023 cost pennies today, making agent automation economically viable for routine business processes
- Enterprise trust in AI has shifted from scepticism to urgency. Companies are no longer asking 'should we use AI?' but 'how fast can we deploy it?'
- The agentic AI framework ecosystem (LangGraph, CrewAI, AutoGen) has proven the architectural patterns, but exposed the need for a visual, no code layer on top
How We're Different From What's Already Out There
We're not the first to think about no code AI. But most existing tools fall into one of two camps: simple chatbot builders that let you create conversational flows (not autonomous agents), or developer focused frameworks that call themselves 'low code' but still require significant technical knowledge.
Our platform sits in the gap between these two. We're building for the operations manager who knows exactly what process they want to automate but shouldn't need to learn Python to do it. The visual builder provides the power of a full agent orchestration framework (branching logic, parallel execution, error handling, tool integration, memory management) wrapped in an interface that anyone can use.
We think of it like this: Figma didn't replace designers. It made design accessible to entire teams. Our builder won't replace AI engineers. It will let business teams build and iterate on agent workflows themselves, with engineering providing oversight and custom integrations where needed.
The Technical Architecture (For Those Who Want to Know)
Under the hood, the platform compiles visual workflows into an execution graph that runs on our agent runtime. We're model agnostic: you can use GPT 4o, Claude, Gemini, or open source models depending on your requirements and data sensitivity. Each agent execution is fully traced and logged, so you get complete observability into every decision the agent made, every tool it called, and every output it produced.
Integrations use a standardised connector framework. We're building first party connectors for the most common B2B tools, and a developer SDK for building custom connectors. This means your agents can interact with virtually any system that has an API, which in 2025 is nearly everything.
Where We Are Right Now
We're currently in active development. The core visual builder, agent runtime, and initial set of integrations are taking shape. We're working with a small group of design partners (B2B companies across professional services, SaaS, and e-commerce) to validate the product against real workflows before we open it up more broadly.
We're not rushing to launch something half baked. We've seen too many AI tools that demo well but fall apart in production. Our focus is on reliability, observability, and the kind of guardrails that businesses need before they trust an AI agent with real customer interactions and real data.
Why We're Sharing This Now
Transparency matters to us. We're not a stealth startup sitting on this. We're a development studio that builds products for businesses every day, and this is the biggest thing we've ever built for ourselves. We're sharing it because we want feedback from the people who would actually use it.
If you're a business leader who's been thinking about AI agents but hasn't pulled the trigger because it seems too complex or too expensive, we're building this for you. If you're an operations manager drowning in repetitive workflows that could clearly be automated, we're building this for you. If you're a CTO who wants to give your business teams AI capabilities without turning every request into an engineering sprint, we're building this for you.
The Future of Work Is Agentic
We believe we're at the very beginning of a fundamental shift in how businesses operate. Over the next decade, every company will have AI agents handling significant portions of their workflows. Not as a competitive advantage, but as a baseline expectation. The companies that start building this capability now will have compounding advantages in efficiency, speed, and customer experience.
The barrier shouldn't be technical complexity. The barrier should only be whether a workflow is worth automating. That's the future we're building toward.
Want early access to the platform or want to explore how agentic AI could transform your business operations? Get in touch with our team. We'd love to talk.
Cover photo by Andrea De Santis on Unsplash
