I'm Building Fewer AI Agents Than Ever - Here's Why
What I've Been Building in the Background That People Want
Building AI agents professionally has taught
and I one thing: most people are so blinded by the hype, they’re chasing complexity they don’t need — and ignoring far simpler, smarter options sitting right in front of them.It’s like everyone’s trying to recreate Jarvis.
They want agents that can reason, reflect, and autonomously act — but in reality, most business problems don’t need autonomy.
They need precision.
They need structure.
For a couple of months now, I’ve been focused on something different. And today, I’m going to show you what that looks like.
Want to understand why the entire industry has got the concept of AI agents backwards?
Let’s get into it.
The Problem With AI Agents in 2025
LinkedIn has been telling us all 2025 was going to be the Year of the Agent.
Guess what?
It’s not.
Why?
Because when you try to deploy "autonomous" AI agents in the real world, there’s some pretty big hills to climb.
Customers Have No Idea What They're Buying
If you’re a regular reader, you’ll have heard me complain about the number of meetings I’ve been to in the last 9 months where my primary objective after 10 minutes has been to gracefully qualify out of an opportunity.
Most of the time it’s because a prospect will utter the magic words of “We need AI Agents in the business”, or some variation of that line.
That aside, the real problem is far worse.
Most people think "AI agent" means "ChatGPT that does stuff automatically".
They don't understand that agents require significant infrastructure changes.
They have no concept that a simple "scheduling agent" might need 6-month implementation timelines.
They're shocked when they learn about vector database management, fail-safe systems and how much human oversight really is needed.
But here's what really drove this home for me: Last week, I spoke with a computer scientist who perfectly articulated the disconnect between what the industry is building and what professionals actually need.
When I started explaining agent architectures — vector stores, memory systems, reasoning frameworks — her response was immediate: "I don't want to learn about all this memory reasoning governance... I want to use the tools that are presented to me."
This is a professional with a computer science background. If she's not interested in complex agent infrastructure, who is?
The answer became clear when she continued: "Everyone's talking about attempting to build agents but they're not using vector store memory, they're not using context, they're not using system level thinking — they're building automations."
She nailed it. The market is calling everything an "agent" when most solutions are just expensive automations wrapped in marketing hype.
No Customer Understands the Hidden Costs
Then there’s the money side of things.
Every agent deployment is actually a business process transformation project. That requires change management.
Despite the "fully autonomous" marketing, agents need constant human supervision. That’s more human labour, not less.
You need a new breed of DBA to manage vector stores in production. More specialist human labour (again) when they’ve been educated via LinkedIn that we should be shedding jobs, not creating them.
Introducing agents into your technical stack increases your infrastructure footprint, it doesn’t reduce it.
Once you’ve addressed these four points in a Zoom call, there’s usually a few blank faces coming back at you.
But the real validation came from that same conversation with the computer scientist. When I mentioned the complexity and costs involved, her response was telling: "Haven't got time for that. We haven't got the budgets for it. We're not going to go and build something like that."
This is the reality gap the industry refuses to acknowledge. Professionals want AI solutions that work within their existing constraints — time, budget, and technical capacity. They don't want to become AI infrastructure specialists, and they don’t want to accept that this is a truth in the field of agents.
The Dirty Secret About "Autonomous" Agents
They're not autonomous. At all.
Every agent system I've built requires:
Human approval/intercept workflows for high-stakes decisions
Constant monitoring
Regular reconfiguration as business contexts change
Fallback procedures when the agent inevitably encounters edge cases
And it's getting worse.
As more companies rush into AI agents, the gap between expectation and reality is widening.
During the past few months I’ve spent more time managing client disappointment and taking on less work than actually building anything because clients don’t get it. The promise of autonomy, driven by hype and executive YouTube education, is creating more problems than it solves.
The Meeting That Broke My Brain
About 8 weeks ago, I had a meeting that hit me for six.
My friend James runs a recruitment firm. He'd been hearing all the AI agent hype and called me with what was quite the wish list.
"Can you build me a meeting preparation agent? I want it to research candidates and clients before calls, create agendas, give me talking points. There’s a few tools out there that do some of this kinda thing, but I want something tailored to my needs, something flexible. Something I can use now, but something I can update and will grow with me."
I started sketching the architecture.
Then James interrupted me: "I just want to be better prepared for meetings. I already use Claude every day. Can't you just make it smarter?"
That's when it hit me. James didn't need an agent.
He needed intelligence.
Everything I’d thought about building to meet this need was just expensive packaging around what Claude could already architecturally accommodate.
So I tried something completely different.
What I Built Instead: Intelligence Without Infrastructure
James represents thousands of professionals who are caught between basic AI tools that don't meet their professional needs and complex agent systems that require technical expertise they don't have.
They don't want to become AI engineers. They want to become more effective professionals using AI intelligence.
Instead of building an expensive agent, I spent two weeks creating something that didn't exist yet: a complete AI application that runs entirely inside a single environment.
No infrastructure.
No integrations.
No change management.
No three-month timeline.
No big expense.
Just sophisticated instructions that transformed Claude into a professional meeting intelligence system:
Gated onboarding that forces context collection before any analysis
Multi-source research protocols using web search and social intelligence
Relationship mapping that identifies competitive dynamics and mutual connections
Strategic agenda generation with talking points tailored to specific objectives
Professional artifacts including visual diagrams and risk assessments
And the key benefit of taking this approach: My friend didn't have to change anything about how he worked—he just got dramatically better at the work he was already doing.
Total cost: $0 in infrastructure.
Total time: Copy, paste, answer questions.
That's when I realised I wasn't just solving James's problem. I was discovering an entirely new category of AI product for which there are hundreds of problems waiting to be solved.
The Birth of "Professional Intelligence Systems”: Why Infrastructure Is Often the Wrong Answer
What I created is what I'm calling a Professional Intelligence System - a sophisticated AI application that runs entirely within an existing interface, using what I call Gated Intelligence Architecture.
Here's why this approach solves problems that traditional agents can't:
The Usual AI Agent Development Path:
❌ Months of labour
❌ Change management requirements
❌ Ongoing infrastructure costs
❌ Specialised technical talent requirements
❌ Tool limitations (in many cases, especially low code)
❌ Human oversight complexity
The Professional Intelligence System:
✅ Immediate deployment (copy/paste)
✅ No code
✅ Works within existing workflows
✅ Zero new infrastructure (unless you want an integration with something)
✅ Transparent and fully customisable
✅ If you want to integrate with other sources, you can.
Using the Gated Intelligence pattern, I was able to build the exact system James needed.
This makes a Professional Intelligence System more than just a clever prompt. It's a complete behavioral framework that delivers intelligence without complexity.
What the System I Built Looks Like
The result of what I developed is a Meeting Intelligence System.
So you can see what it looks like, for fun, I’ve set it up to help me prepare for a meeting to pitch Satya Nadella, CEO of Microsoft 😂.
How to Make a Billionaire CEO Say 'Tell Me More' in 10 Seconds
Ever wonder how some people walk into meetings with Microsoft's CEO and immediately capture attention while others get the polite nod-and-forget treatment?
This isn't luck or charisma – it's professional intelligence at work. The Meeting Intelligence System analysed Satya Nadella's recent Build 2025 keynote where he specifically called persistent memory "conspicuously missing" from current agents.
Instead of generic small talk, you open with the exact problem he publicly acknowledged as critical – positioning yourself as the solution provider, not another vendor.
When You Have 35 Minutes to Change a CEO's Mind
What's the difference between demos that get polite applause and those that lead to partnerships?
Don’t waste precious minutes showing features nobody cares about. The Meeting Intelligence System analysed Satya's recent interviews where he predicted "the collapse of SaaS applications" in the agentic era.
Instead of generic product demos, you're guided to show exactly how your technology enables his public vision – creating an "of course we need this" moment rather than a "that's interesting" response.
How to Make Your Solution Feel Like Microsoft's Idea
Ever notice how some companies get acquired while others get competed with?
Instead of selling a product, you're offering to accelerate the success of their existing massive investment – making your solution feel like an extension of their strategy rather than a new decision.
Each of these screenshots demonstrates how the Meeting Intelligence System transforms ordinary meeting preparation into strategic positioning that changes outcomes.
This isn't about basic research – it's about professional-grade intelligence that positions you for success before you say a single word.
And I got all this done in less than 10 minutes.
The Optional Upgrade - What Satya Wants
The Meeting Intelligence System works perfectly as designed - immediate deployment, zero infrastructure, enterprise-grade intelligence within existing workflows. But there's an optional upgrade that transforms it from a sophisticated tool into something far more powerful: a learning organisational intelligence system.
As you’ve seen in the walk through, the biggest problem facing business AI in 2025 isn't model capability or user interface design. It's persistent memory.
Every AI interaction starts from scratch, unable to learn from previous analyses, successful strategies, or institutional knowledge.
This is where the Memory as a Service upgrade changes everything.
The Enterprise Memory Challenge
Let’s switch gears for a minute and think about a different use case.
Consider a Sales Operations Analyst running this query: "Show me all deals over $50K in the Northeast territory stuck in 'Proposal' stage for more than 30 days, cross-reference with competitive win/loss data, and suggest next steps."
With a standard Professional Intelligence System, Claude provides excellent analysis by synthesizing data from Confluence and a CRM database. But it can't remember that three months ago, this exact pattern led to successful competitive displacement using specific messaging frameworks, or that similar deals in Q2 responded well to particular stakeholder engagement tactics.
The Memory Upgrade Solution
Adding a Memory as a Service component can provide persistent institutional memory. Now the same query returns not just current data analysis, but accumulated organisational intelligence:
Historical pattern recognition: "This situation mirrors 23 previous deals - here's what worked"
Methodology refinement: "Updated risk assessment framework based on 6 months of successful applications"
Competitive intelligence evolution: "Competitor X changed pricing strategy in Q3 - adjusted counter-positioning"
Stakeholder psychology mapping: "Similar customer profiles responded to these specific value propositions"
Technical Implementation: Zero Disruption
The memory upgrade maintains the core design philosophy - no code, immediate deployment, existing workflows. It's simply an additional connection that provides API access to vector-based organisational memory hosted on AWS.
The Professional Intelligence System continues to work identically, but now each interaction builds on accumulated institutional knowledge rather than starting fresh.
Your sales analyst's Claude project evolves from a smart assistant into a repository of organisational sales expertise that becomes more valuable with every interaction. Success strategies get captured, competitive insights accumulate, and methodology refinements persist across the entire organisation.
Will Professional Intelligence Systems Take Off?
The conclusion I’ve come to over the past few months is that in many scenarios, we think about AI completely backwards.
The industry keeps trying to build better containers for AI (LLMs, agents, platforms, integrations), when what people actually need is better intelligence within the containers they already have.
The lesson from my friend is that he didn't need a new system. He needed his existing system to be smarter.
Every executive I know uses ChatGPT or Claude daily. They don't want another tool to learn, another platform to manage, another subscription to justify. They want the tools they already use to deliver professional-grade results.
That's exactly what I think Professional Intelligence Systems can do. Packaging AI capabilities, making them portable, transparent, customisable applications that work inside the tools people already use. No infrastructure. No change management. No six-month projects.
What I'm Building Next
I'm not giving up on agents, far from it.
But there’s little doubt I’ll be continuing to say “no” to opportunities until customers come to their senses about the realities of the technology.
In the meantime, I'll be investing a lot more of my time building a library of Professional Intelligence Systems focused on solving very specific problems that give us humans the force multiplier benefit of AI we’ve all been promised.
So, 2025. The Year of the Agent right? Let me know what you think in the comments.
Until the next time,
Chris