Introduction: Beyond Co-Pilot
Fresh from Dreamforce in October, I shared my initial thoughts about Agentforce. The community’s first reaction was, understandably, skepticism – “Isn’t this just Co-pilot rebranded?”
I get it. Having spent my career as a professional AI skeptic, I’ve always focused on cutting through the hype to find pragmatic solutions that deliver real business value.
But this is different. Agentforce isn’t just another rebranded AI tool – it represents something fundamentally new. It’s the first enterprise-scale governed system that truly harnesses the power of agentic AI. And that distinction matters.
Why This Deep Dive Matters
The launch dust is settling and our customers are already pushing Agentforce into production. Now’s the perfect time to peek under the hood and really understand what we’ve created here. We need to grapple with:
- What this technology really is
- Why it represents such a fundamental shift
- How to harness its transformative potential
I know what you’re thinking – “Oh great, another ‘game-changing’ technology announcement.” But stick with me here, we’re not just talking about a shiny new feature or another three-letter acronym to add to your LinkedIn profile. We’re looking at an entirely new capability stack. One that’s already driving real organisational change and in some cases, transforming entire business models. And yes, I’m as surprised as you are about how quickly that happened.
This is going to be the first in a series of deep dives where I’ll explore:
- The fundamentals of agent design (or “how to make LLMs do your bidding”)
- The architecture of LLM-powered systems (without getting lost in the matrix)
- How to achieve excellence in the new paradigm of generative AI (and keep your sanity intact)
Why focus on Agentforce? Well, after six months of hands-on development I’ve become convinced of something rather extraordinary – the combination of Agentforce, Einstein, and Data Cloud represents something unprecedented. It’s not just another AI platform; it’s the first truly production-ready, enterprise-scale system for harnessing generative AI that actually delivers on its promises. And in the world of enterprise AI, that’s about as rare as a bug-free first deployment.
What Makes an Agent?
Right about now, you’re probably seeing the term “agent” plastered across every tech blog and LinkedIn post. But instead of diving into yet another Agentforce feature list, let’s ask the real question that’s keeping CTOs up at night: what exactly is an agent, and why has it become the hottest thing in IT since cloud computing?
The Historical Context
Here’s the thing that might blow your mind – this isn’t actually new technology. Plot twist! I’ve been neck-deep in agent-based AI and its precursor patterns (like cellular automata) for most of my professional life. It’s my personal catnip, if you will. And what I find deliciously ironic is that just because we’ve figured out how to implement agent-based patterns using LLMs and generative AI, everyone’s acting like we’ve invented fire.
The reality? This is actually one of the most well-established, thoroughly researched areas in AI and artificial life. It’s like that indie band you loved before they hit mainstream – it’s been doing great work for decades, it just needed the right moment (and maybe a better agent, pun intended) to hit the big time.
But – and this is crucial – just because it has a prestigious academic pedigree doesn’t mean it’s only for the PhD crowd. That’s the beauty of where we are now. We’ve reached a point where we can take these battle-tested concepts and make them accessible to anyone building on our platform.
The key is to ground ourselves in the fundamentals first – understand what makes agent-based behaviour different from your garden-variety machine learning model. Once you have that foundation, you’ll be able to see for yourself whether Agentforce delivers on its promises. And spoiler alert: this is where things get really interesting.
The Anthropic Connection
Now, if you’re wondering why I’m so confident about all this, let me introduce you to some folks who really know their stuff – Anthropic. They are the creators of Claude, the AI chatbot that somehow managed to become everyone’s favourite digital colleague in record time.
Claude and the whole Anthropic crew have joined the Salesforce family of LLMs and they’re not just sitting on the sidelines – they’re increasingly becoming the powerhouse under Agentforce’s hood.
Just before the break Anthropic dropped what I can only describe as a beautiful early Christmas present – a comprehensive article that maps out exactly how people are using LLMs in the wild. We’re talking workflows, chains, and yes, our star of the show: agent-based patterns.
Think of it as a field guide to LLMs in their natural habitat and what they’re seeing validates everything we’ve been building towards with Agentforce.
The Core Components
Alright, let’s get our hands dirty and look at what makes an intelligent agent tick. If we’re going to claim this is more than just another chatbot with a fancy name tag, we better understand what’s actually different under the hood. (Spoiler: it’s not just better marketing copy.)
Beyond Simple Memory
Let’s start with the basics – your garden-variety chatbot really only needs one thing: memory. It needs to remember what you said five minutes ago. The Anthropic paper identifies two critical components that transform a simple chatbot into something far more capable: retrieval and tools. And before you roll your eyes at another pair of buzzwords, let me explain why these are the secret sauce that takes us from “glorified chatbot” to “actual intelligent agent.”
These two additions give an LLM superpowers at runtime (that’s tech-speak for “while it’s doing its thing”). We’re talking about the ability to:
- Query databases
- Rummage through PDFs and unstructured data
- Use external tools and APIs
- Actually make real things happen and – here’s the kicker – wait to see what happened before deciding what to do next
But here’s the mind-bending part; this architecture gives an AI system the ability to use tools and learn from their results in real-time. We’ve graduated from “AI that can help with your homework” to “AI that can actually do the homework, check its work, and explain where it went wrong.”
The Power of Prompt Engineering
Let’s talk about one of the most fascinating evolutions in the LLM space – how we went from wrestling with individual prompts to building sophisticated prompt architectures. And no, I’m not talking about those “write a poem about DevOps in the style of Shakespeare” prompts, I’m talking about enterprise-grade prompt engineering that makes LLMs actually useful in production.
The Anthropic paper lays out a beautiful progression that mirrors what many of us discovered through trial and error. It turns out that getting the most out of an LLM isn’t just about crafting the perfect single prompt – it’s about building intelligent sequences of prompts that work together.
Think of it like this: instead of trying to get a PhD-level analysis from a single, massive prompt, we learned to break things down into smaller, more manageable chunks. Each prompt has a specific job, and they work together like a well-oiled machine.
Understanding Chain Patterns
This is where it gets interesting. The paper shows how people naturally evolved toward what we now call “chain patterns.” Imagine you’re teaching someone a complex task – you wouldn’t dump all the information at once. You’d break it down into steps:
- First, understand the context
- Then, plan the approach
- Next, execute each step
- Finally, verify and adjust
That’s exactly what modern prompt chains do. Each step in the chain is a separate LLM interaction, carefully designed to:
- Handle one specific aspect of the larger task
- Pass its insights forward to the next step
- Build upon previous results
- Maintain context without getting overwhelmed
The beauty of this approach is that it mirrors how humans actually solve complex problems. We don’t solve everything in one giant leap – we break things down, we iterate, we check our work. And just like human expertise, these chains can be templated and reused across similar problems.
This pattern emerged organically across the industry because it works. Whether you’re building customer service automation, code analysis tools, or data processing pipelines, this fundamental architecture of connected, purposeful prompts has proven to be the key to reliable, production-grade LLM applications.
The Loop: What Makes it an Agent
And now we come to the real magic – the thing that transforms a clever chain of prompts into a true agent. If you look at the diagrams in the Anthropic paper, you’ll notice something that makes one architectural pattern stand out from all the others. It has a loop.
In every talk I’ve given on this topic, I end up waving my hands in circles until people probably think I’ve lost it. But there’s a method to my madness – I’m trying to hammer home that this isn’t just about sequence, it’s about iteration. This concept of a feedback loop isn’t just some neat trick we discovered with LLMs – it’s been the cornerstone of agent-based systems for over 40 years, showing up everywhere from simulations of termite colonies to studies of honeybee behavior.
The Environment as Memory
Here’s where it gets mind-bending: when we allow a decision-making system to modify its environment and then observe those modifications, the environment itself becomes a form of memory. It’s like leaving yourself sticky notes around your house – the environment becomes both the canvas for your actions and a record of what you’ve done.
This isn’t just a cute analogy – it’s a fundamental pattern that emerges in all sorts of complex systems. When an agent reads the environment again and sees the changes it’s made, that information directly influences its next decision. It’s exactly the same pattern we see in human decision-making loops and it’s what makes the difference between a system that can follow instructions and one that can actually adapt and learn.
The React Pattern
The technical implementation of this loop has evolved into what we now call the React pattern (no relation to the JavaScript framework, though the naming is amusingly apt). Think of it as a structured way to make LLMs actually react to the world around them, rather than just responding to prompts in isolation.
This pattern allows an AI system to:
- Observe the current state of things
- Think about what it’s seeing
- Plan what to do next
- Take action
- See what happened
- Adjust its approach based on results
The beauty of this approach is that it enables real-time adaptation – not through traditional machine learning (where we feed it massive datasets of successes and failures), but through something much more akin to how humans learn on the job:
- Try something
- See if it worked
- Adjust your approach
- Repeat
This is what separates a true agent from a simple chain of prompts. The chain knows what to do next because we told it. The agent knows what to do next because it observed the results of its last action and made a decision.
To put it as simply, profoundly and beautifully as it was taught to me: reasoning is a loop. To build a machine that can reason, we must build a system that works in loops.
From Patterns to Production: The Atlas Reasoning Engine
So we’ve covered the theory – the patterns that make an agent truly agentic. But here’s where the rubber meets the road: how do you actually implement these patterns in a way that works at enterprise scale? This is where Agentforce’s Atlas Reasoning Engine comes into play, and honestly, this is where things get exciting.
Remember that loop pattern we talked about? The Atlas Reasoning Engine is Agentforce’s implementation of that pattern, but with enterprise-grade steroids. Think of it as the conductor of an orchestra, but instead of musicians, it’s coordinating a complex dance of:
- Real-time decision making
- Dynamic resource allocation
- Contextual awareness
- Execution monitoring
The Atlas Reasoning Engine is what makes this orchestration possible. It’s the technological answer to the question: “How do we take these beautiful theoretical patterns and make them work in the messy real world of enterprise IT?” And the answer turns out to be pretty elegant.
Beyond Simple Task Execution
What makes the Atlas Reasoning Engine special isn’t just that it can execute tasks – lots of systems can do that. It’s that it can:
- Handle both quick wins and marathon sessions
- Adapt its approach based on intermediate results
- Manage complex, multi-step processes
- Scale from simple automation to complex workflow orchestration
This isn’t just about running through a predefined sequence of steps. The engine is constantly evaluating, planning, and adjusting – exactly like that React pattern we discussed, but implemented in a way that can handle enterprise-scale workloads and security requirements.
The Human Element
But here’s what really sets this implementation apart: it’s not just about the technology. The Atlas Reasoning Engine was built with a fundamental understanding that in enterprise environments, the most important part of the environment is actually the human in the loop – the customer, the employee, the stakeholder.
Every interaction becomes part of the environment that the agent observes and learns from. Every response helps it navigate toward better outcomes. This isn’t just a bot following a script – it’s an agent engaging in genuine dialogue, where the human’s reactions and inputs are crucial parts of the decision-making process.
This is what transforms it from a simple automation tool into something far more powerful: a system that can actually collaborate with humans to solve problems in real-time. And unlike traditional chatbots that try to anticipate every possible conversation path, this system can effectively ‘improv’ each conversation – exactly like a human would.
The Agent as a User: A Paradigm Shift
And now we come to what might be the most mind-bending part of this whole story. It’s the thing that kept me up at night when I first really understood its implications. Here it is: in Agentforce, the agent itself gets assigned a user identity.
“Big deal,” I hear the platform veterans say, “we’ve been creating system integration users since before AI was cool.” But this is different. Fundamentally, radically different.
Why This Isn’t Just Another System Account
See, with traditional integration users, we’re dealing with what I call “predictable automatons” – systems that do exactly what they’re programmed to do, nothing more, nothing less. The scope of their behaviour is clearly defined, their actions perfectly predictable.
But we’ve spent this entire article talking about how agents are different. They have discretion. They make choices. They adapt. They learn. Giving an agent a user identity isn’t like giving credentials to a predictable system – it’s more like hiring a new employee who can actually think for themselves.
Behavior-as-a-Service: The New Frontier
This is why we’ve started talking about “Behavior-as-a-Service” – because that’s literally what we’re creating. An agent:
- Crafts its own execution plans (no more rigid decision trees)
- Experiments with different approaches (sometimes succeeding, sometimes failing)
- Explores possibilities we might not have considered
- Makes mistakes, learns from them, and adapts its strategy
It’s not just executing code – it’s exhibiting behavior. And that’s a paradigm shift that’s going to require some serious rethinking of how we approach system design and security.
The Philosophical Elephant in the Room
Now, I can already hear the heated debates starting: “Are we creating virtual employees? Let me stop you right there – that’s not the conversation I’m interested in having (at least not before my second coffee). What I am interested in is the practical reality staring us in the face: when we build systems with agents, we are, for the first time, creating entities that need to be treated as first-class users of our systems because that’s literally how they function.
This is about system design. When an entity can make discretionary decisions about how to use its access and permissions, it needs to be treated as a user, full stop. And this is exactly where Salesforce’s decades of experience becomes our secret weapon.
Think about it – what company has spent more time thinking about enterprise-grade user management, security, and governance at scale? This isn’t just about managing permissions; it’s about the entire trust layer that Salesforce has spent 20+ years perfecting.
The same infrastructure that lets global enterprises manage millions of users, enforce sophisticated security policies, and maintain compliance across dozens of regulatory frameworks – that’s the foundation Agentforce is built on. By implementing agents as users within Salesforce, we’re not creating a security nightmare – we’re leveraging one of the most sophisticated, battle-tested user management systems on the planet. What might be a terrifying proposition on other platforms becomes a natural extension of what Salesforce already does better than anyone else. It’s like we’ve been inadvertently preparing for the age of agentic AI this whole time.
Conclusion: Why Agentforce Matters
Let’s pull all these threads together. We started with patterns – decades-old, battle-tested concepts about what makes an agent truly agentic. We explored how modern LLM technology finally gives us the tools to implement these patterns at scale. And then we looked at how Agentforce brings it all together in a way that’s not just theoretically sound, but practically implementable.
But here’s the real kicker: Agentforce isn’t just another AI product that happens to be built on Salesforce. It’s a product that could only have been built on Salesforce, because it leverages:
- Twenty years of enterprise-grade user management and security
- The incredible foundation laid by Data Cloud
- The sophisticated prompt engineering capabilities of Einstein
- A decade of real-world AI implementation experience
- The world’s most trusted enterprise platform
This isn’t just another overnight AI sensation, it’s built on years of groundwork. While others were rushing to bolt AI capabilities onto existing systems, Salesforce was quietly building the robust, multi-layered infrastructure that would make true enterprise-scale agent systems possible.
The truth is, other agent solutions will come and go because they don’t have the foundation to make those patterns work in the real world. They’re trying to build skyscrapers without proper foundations.
That’s why I’m genuinely excited about Agentforce. When I’m designing agent systems, I get to focus on the interesting parts – the agent’s behaviour, its capabilities, its interactions. I don’t lose sleep over:
- Whether my retrieval system will scale
- If my security model is robust enough
- How to handle enterprise compliance
- Whether my tools will play nice together
Because all of that is handled by the platform that’s been solving these problems for enterprises longer than some AI developers have been alive. (Sorry, I had to slip in one last dad joke.)
In the end, that’s what makes Agentforce different. It’s not just that it implements the right patterns – though it does. It’s not just that it has powerful capabilities – though it has those too. It’s that it brings all of this together on a platform that was inadvertently preparing for the age of agentic AI for the past two decades.
And that’s not just marketing speak – that’s the engineering reality that’s going to make the difference between AI systems that sound good in demos, and AI systems that actually transform how enterprises work.
Welcome to the age of enterprise agents. We’ve been preparing for this longer than we knew.
