AI Strategy
The 5 Levels of Agentic AI: Where Does Your Business Sit?
Most businesses are using AI in some form. But there is a massive gap between asking ChatGPT a question and deploying production-grade AI agents that run your operations. Here is how to figure out where you are, and what comes next.
Curated by Matt Perry
CTO
The AI Maturity Gap Is Real
A recent LinkedIn discussion sparked by our CTO Matt Perry highlighted something we see every day: there is a huge spectrum of how businesses use AI. Some are just dipping a toe in with ChatGPT. Others are running fully orchestrated multi-agent systems that handle core operations autonomously.
Most businesses sit somewhere in the middle, often unsure of what the next step looks like or whether they are ready for it.
After working with dozens of companies at every stage, we have mapped out five distinct levels of agentic AI maturity. Understanding where you sit helps you plan what comes next, without overreaching or underinvesting.
Level 1: The Chat Era
This is where most businesses start. Someone on the team discovers ChatGPT, Claude, or Gemini and starts using it for everyday tasks. Writing emails. Summarising documents. Brainstorming ideas. Getting quick answers to technical questions.
There is nothing wrong with this. It is genuinely useful. But it is also entirely manual. Every interaction requires a person to type a prompt, read the response, and decide what to do with it. The AI has no memory of your business, no access to your systems, and no ability to take action on your behalf.
You are at Level 1 if: Your team uses AI chat tools on an ad-hoc basis for individual productivity. There is no shared strategy, no integration, and no automation.
Level 2: The Chatbot
The natural next step is to put AI where your customers or staff can interact with it directly. This usually means adding a chatbot to your website, internal knowledge base, or customer service workflow.
Modern chatbots powered by large language models are dramatically better than the rigid, menu-driven bots of five years ago. They can understand natural language, pull from your documentation, and handle a surprising range of queries without human intervention.
But they are still reactive. They wait for someone to ask a question. They operate in a single channel. And they typically cannot take meaningful action beyond providing information.
You are at Level 2 if: You have deployed an AI-powered chatbot that handles customer queries or internal questions, but it does not connect to your backend systems or trigger workflows.
Level 3: No-Code Workflows
This is where things start getting interesting. Tools like Zapier, Make, and n8n let you connect AI capabilities to your existing business tools without writing a single line of code. You can build workflows that trigger automatically: when an email arrives, summarise it and log the key details in your CRM. When a form is submitted, classify the enquiry and route it to the right team.
No-code workflows bring genuine automation. Tasks that used to require human attention now happen in the background. Your team gets time back. Response times improve. Consistency goes up.
The limitation is flexibility. No-code tools work brilliantly for straightforward, linear processes. But when logic gets complex, when you need conditional branching based on nuanced context, or when you need to handle edge cases gracefully, you start hitting walls.
You are at Level 3 if: You have built automated workflows using no-code platforms that incorporate AI for tasks like classification, summarisation, or content generation.
Level 4: System Integrations
At this level, AI is woven into your existing business systems. It is not a separate tool your team switches to. It is embedded in the software they already use every day.
This might look like AI-powered search across your internal documentation. Automated data extraction from invoices that flows directly into your accounting system. Intelligent routing of support tickets based on content analysis. Predictive analytics built into your dashboards.
The key difference from Level 3 is depth. These are not simple workflow triggers. They are purpose-built integrations that understand your data model, respect your business rules, and operate within your existing security and compliance framework.
This is also where the build-vs-buy decision becomes critical. Off-the-shelf AI features in your existing software might cover 80% of what you need. The remaining 20%, the part that is specific to how your business actually works, often requires custom development.
You are at Level 4 if: AI capabilities are integrated into your core business systems, operating on your data with custom logic that reflects your specific processes.
Level 5: Multi-Agent Orchestration
This is the frontier. Instead of a single AI handling a single task, you deploy multiple specialised agents that work together to accomplish complex objectives. One agent researches. Another analyses. A third drafts. A fourth reviews. An orchestrator coordinates the whole process.
Tools like Anthropic's Agent SDK, LangGraph, and CrewAI make this possible. You define each agent's role, capabilities, and constraints in code. You give them access to specific tools and data sources. Then you let them collaborate on tasks that would be impractical for a single model to handle alone.
The results can be remarkable. A well-orchestrated agent system can handle end-to-end processes that previously required multiple people across multiple days. Research, analysis, content creation, quality assurance, all running autonomously with human oversight at key checkpoints.
But this level demands real engineering discipline. You need proper error handling, observability, testing, and deployment pipelines. You need to think carefully about security, data access, and failure modes. This is not vibe coding. This is production software development.
You are at Level 5 if: You have deployed coded multi-agent systems that orchestrate complex workflows autonomously, with proper engineering practices, monitoring, and human oversight.
A Note on Software Development: Vibe Coding vs Context Engineering
There is a parallel maturity curve happening specifically in software development teams adopting AI.
At one end, you have vibe coding: asking an AI to generate code based on a loose description, accepting whatever comes back, and hoping for the best. It is fast. It feels productive. And for prototypes or throwaway scripts, it can work fine.
At the other end, you have context engineering: carefully structuring your prompts, providing architectural context, defining constraints, running multiple review passes, and validating outputs against your standards. This is what Matt described in his LinkedIn post, combining AI's speed with rigorous engineering practices so that the output is genuinely production-ready.
The difference matters enormously. Vibe coding produces code that works in demos. Context engineering produces code that works in production, at scale, under load, when things go wrong. As Matt put it, when you combine AI tooling with experience, judgement, and discipline, "the AI slop problem disappears very quickly."
Where Original Objective Fits In
We work with businesses at every level of this maturity curve. If you are at Level 1, we can help you identify where AI will have the biggest impact and build a practical roadmap. If you are at Level 3 and hitting the limits of no-code tools, we can build custom integrations that go deeper. If you are ready for Level 5, we have the engineering team to architect and deploy multi-agent systems that actually work in production.
Our focus is on small and medium-sized businesses. We believe every company deserves access to production-quality AI systems, not just enterprises with seven-figure technology budgets. The tools have matured to the point where this is genuinely achievable, but only if the implementation is done properly.
We are not interested in building demos that impress in a meeting but fall apart in practice. We build systems that run reliably, scale sensibly, and deliver measurable results.
What Should You Do Next?
Be honest about where you sit on this scale. There is no shame in being at Level 1. Every business that is now running sophisticated AI systems started there.
The important thing is to have a clear view of where you want to get to, and a realistic plan for getting there. Jumping from Level 1 to Level 5 rarely works. But moving methodically from one level to the next, building capability and confidence as you go, almost always does.
If you want help figuring out your next step, book a free intro call with our team. We will walk you through what is realistic for your budget, your team, and your goals.
Curated by Matt Perry
CTO
The AI Maturity Gap Is Real
A recent LinkedIn discussion sparked by our CTO Matt Perry highlighted something we see every day: there is a huge spectrum of how businesses use AI. Some are just dipping a toe in with ChatGPT. Others are running fully orchestrated multi-agent systems that handle core operations autonomously.
Most businesses sit somewhere in the middle, often unsure of what the next step looks like or whether they are ready for it.
After working with dozens of companies at every stage, we have mapped out five distinct levels of agentic AI maturity. Understanding where you sit helps you plan what comes next, without overreaching or underinvesting.
Level 1: The Chat Era
This is where most businesses start. Someone on the team discovers ChatGPT, Claude, or Gemini and starts using it for everyday tasks. Writing emails. Summarising documents. Brainstorming ideas. Getting quick answers to technical questions.
There is nothing wrong with this. It is genuinely useful. But it is also entirely manual. Every interaction requires a person to type a prompt, read the response, and decide what to do with it. The AI has no memory of your business, no access to your systems, and no ability to take action on your behalf.
You are at Level 1 if: Your team uses AI chat tools on an ad-hoc basis for individual productivity. There is no shared strategy, no integration, and no automation.
Level 2: The Chatbot
The natural next step is to put AI where your customers or staff can interact with it directly. This usually means adding a chatbot to your website, internal knowledge base, or customer service workflow.
Modern chatbots powered by large language models are dramatically better than the rigid, menu-driven bots of five years ago. They can understand natural language, pull from your documentation, and handle a surprising range of queries without human intervention.
But they are still reactive. They wait for someone to ask a question. They operate in a single channel. And they typically cannot take meaningful action beyond providing information.
You are at Level 2 if: You have deployed an AI-powered chatbot that handles customer queries or internal questions, but it does not connect to your backend systems or trigger workflows.
Level 3: No-Code Workflows
This is where things start getting interesting. Tools like Zapier, Make, and n8n let you connect AI capabilities to your existing business tools without writing a single line of code. You can build workflows that trigger automatically: when an email arrives, summarise it and log the key details in your CRM. When a form is submitted, classify the enquiry and route it to the right team.
No-code workflows bring genuine automation. Tasks that used to require human attention now happen in the background. Your team gets time back. Response times improve. Consistency goes up.
The limitation is flexibility. No-code tools work brilliantly for straightforward, linear processes. But when logic gets complex, when you need conditional branching based on nuanced context, or when you need to handle edge cases gracefully, you start hitting walls.
You are at Level 3 if: You have built automated workflows using no-code platforms that incorporate AI for tasks like classification, summarisation, or content generation.
Level 4: System Integrations
At this level, AI is woven into your existing business systems. It is not a separate tool your team switches to. It is embedded in the software they already use every day.
This might look like AI-powered search across your internal documentation. Automated data extraction from invoices that flows directly into your accounting system. Intelligent routing of support tickets based on content analysis. Predictive analytics built into your dashboards.
The key difference from Level 3 is depth. These are not simple workflow triggers. They are purpose-built integrations that understand your data model, respect your business rules, and operate within your existing security and compliance framework.
This is also where the build-vs-buy decision becomes critical. Off-the-shelf AI features in your existing software might cover 80% of what you need. The remaining 20%, the part that is specific to how your business actually works, often requires custom development.
You are at Level 4 if: AI capabilities are integrated into your core business systems, operating on your data with custom logic that reflects your specific processes.
Level 5: Multi-Agent Orchestration
This is the frontier. Instead of a single AI handling a single task, you deploy multiple specialised agents that work together to accomplish complex objectives. One agent researches. Another analyses. A third drafts. A fourth reviews. An orchestrator coordinates the whole process.
Tools like Anthropic's Agent SDK, LangGraph, and CrewAI make this possible. You define each agent's role, capabilities, and constraints in code. You give them access to specific tools and data sources. Then you let them collaborate on tasks that would be impractical for a single model to handle alone.
The results can be remarkable. A well-orchestrated agent system can handle end-to-end processes that previously required multiple people across multiple days. Research, analysis, content creation, quality assurance, all running autonomously with human oversight at key checkpoints.
But this level demands real engineering discipline. You need proper error handling, observability, testing, and deployment pipelines. You need to think carefully about security, data access, and failure modes. This is not vibe coding. This is production software development.
You are at Level 5 if: You have deployed coded multi-agent systems that orchestrate complex workflows autonomously, with proper engineering practices, monitoring, and human oversight.
A Note on Software Development: Vibe Coding vs Context Engineering
There is a parallel maturity curve happening specifically in software development teams adopting AI.
At one end, you have vibe coding: asking an AI to generate code based on a loose description, accepting whatever comes back, and hoping for the best. It is fast. It feels productive. And for prototypes or throwaway scripts, it can work fine.
At the other end, you have context engineering: carefully structuring your prompts, providing architectural context, defining constraints, running multiple review passes, and validating outputs against your standards. This is what Matt described in his LinkedIn post, combining AI's speed with rigorous engineering practices so that the output is genuinely production-ready.
The difference matters enormously. Vibe coding produces code that works in demos. Context engineering produces code that works in production, at scale, under load, when things go wrong. As Matt put it, when you combine AI tooling with experience, judgement, and discipline, "the AI slop problem disappears very quickly."
Where Original Objective Fits In
We work with businesses at every level of this maturity curve. If you are at Level 1, we can help you identify where AI will have the biggest impact and build a practical roadmap. If you are at Level 3 and hitting the limits of no-code tools, we can build custom integrations that go deeper. If you are ready for Level 5, we have the engineering team to architect and deploy multi-agent systems that actually work in production.
Our focus is on small and medium-sized businesses. We believe every company deserves access to production-quality AI systems, not just enterprises with seven-figure technology budgets. The tools have matured to the point where this is genuinely achievable, but only if the implementation is done properly.
We are not interested in building demos that impress in a meeting but fall apart in practice. We build systems that run reliably, scale sensibly, and deliver measurable results.
What Should You Do Next?
Be honest about where you sit on this scale. There is no shame in being at Level 1. Every business that is now running sophisticated AI systems started there.
The important thing is to have a clear view of where you want to get to, and a realistic plan for getting there. Jumping from Level 1 to Level 5 rarely works. But moving methodically from one level to the next, building capability and confidence as you go, almost always does.
If you want help figuring out your next step, book a free intro call with our team. We will walk you through what is realistic for your budget, your team, and your goals.
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