So, Corn, I was looking at some of the latest numbers from the Salesforce twenty twenty-six Connectivity Report this morning, and one statistic really jumped out at me. It turns out that even now, in the spring of twenty twenty-six, fifty percent of all enterprise A I agents are operating in total isolation. They are silos. They do not talk to each other, they do not share memory, and they do not contribute to a central knowledge base. It is like having a thousand brilliant employees who are all forbidden from ever speaking to one another. We have spent the last two years obsessed with making models smarter, but we have completely neglected the connective tissue that makes them useful at scale.
Herman Poppleberry here, and you are spot on. It is a massive efficiency leak. We are seeing these incredible returns on investment in some sectors, but the average enterprise is still struggling because they are building these little islands of automation instead of a cohesive ecosystem. It is the classic mistake of thinking the tool is the strategy. Our housemate Daniel actually just finished this massive project trying to map exactly how to fix that. He calls it the Agentic Symphony. It is a complete architecture for agentic systems that focuses on the stuff that turns those isolated islands into a single organism.
I love that name, the Agentic Symphony. It implies a level of harmony and conducting that we just have not seen in most deployments yet. Usually, it is more like an elementary school band where everyone is playing a different song at a different tempo. Daniel published this as an interactive space on Hugging Face and a full repository on GitHub, and I think it is probably the most comprehensive visualization of the agentic stack I have seen to date. It covers fourteen layers and thirty-nine distinct connections. Today, I want to really tear this thing apart. I want to look at what he got right, where the industry is still lagging, and specifically look at these hidden pathways he calls latent value spaces.
It is a deep dive for sure. If you look at the center of his map, he has got the core engine: the prompts, the models, and the inference. But what makes this different from your standard A I diagram is how he treats the periphery. He treats things like safety, observability, and knowledge management not just as add-ons or afterthoughts, but as integral parts of the feedback loop. We have talked about some of the foundational stuff before, like back in episode seven hundred ninety-five when we discussed sub-agent delegation, but this map takes it to a whole new level of architectural maturity. It moves us from chatting with models to architecting agentic ecosystems.
Let us start with the layers themselves because there is some nuance here that most people miss. Usually, when people talk about prompts, they just think about the user typing a message. But Daniel breaks the prompt layer into three distinct parts: the user prompt, the system prompt, and something he calls the vendor prompt. Corn, why is it so important to treat the vendor prompt as its own architectural layer?
Because the model is never truly vanilla, Herman. When you use Claude or G P T four or Gemini, you are not just interacting with a raw weight-and-bias machine. There is a massive, invisible layer of directives from the provider. These are the R L H F alignment instructions, the safety guardrails, and the hidden system prompts that the companies use to shape the model's personality and boundaries. In an enterprise context, if you do not account for that vendor prompt, you are going to run into unexpected behaviors that you cannot explain through your own system prompts. It is a masterstroke to include that because it acknowledges the reality of shared agency between the developer and the model provider. You are never the only one giving the A I orders.
It is almost like a ghost in the machine that you have to design around. If the vendor prompt says "never talk about internal company politics" and your system prompt says "analyze our internal political climate," you are going to get a refusal and you will be scratching your head as to why. And speaking of designing around things, I noticed he placed the Model Context Protocol, or M C P, right at the center of the orchestration loop. We have seen M C P go from an experimental idea in late twenty twenty-four to a full-blown enterprise standard here in twenty twenty-six. The market for M C P servers is projected to hit one point eight billion dollars this year. Does placing it at the center like this reflect how companies are actually building right now, or is that still aspirational?
I think it is the current frontier. If you look at companies like Salesforce with Agentforce or what Anthropic has done with the protocol, M C P has become the nervous system. It is how the brain, the model, actually moves the hands, the tools. Daniel's map shows M C P connecting to the tool registry, then through a human-in-the-loop checkpoint, and finally to the actions. That chain is critical. Ninety-six percent of I T leaders agree that agentic success depends on seamless data integration, and M C P is the first time we have had a standardized language for that. Without it, you are back to writing custom wrappers for every single A P I, which is just not scalable. It is the difference between having a universal plug and having to rewire your house every time you buy a new toaster.
I want to push back a little on his Agents layer, though. He groups orchestration, agents, pipelines, and workflows all together. In our experience, and looking at the current neuro-symbolic trends, there is a growing argument that we should be separating probabilistic reasoning from deterministic execution more strictly. When you put them in the same bucket, do you think we risk making the system too unpredictable?
That is a fair critique. A lot of high-reliability systems now use a symbolic layer to handle the hard logic and only call the L L M for the fuzzy reasoning. Daniel's map does distinguish between them through the connections, though. He shows that pipelines and workflows have tools wired at design time, which is deterministic, whereas agents choose their tools autonomously. But you are right, the industry is moving toward a more rigid separation. If an agent is deciding whether to execute a bank transfer, you do not want that to be a purely probabilistic decision based on what word comes next. You want a deterministic wrapper around that choice. We are seeing a lot of people move toward LangGraph or similar frameworks to enforce those boundaries.
And that leads right into the human-in-the-loop layer. I noticed he has it positioned as a gatekeeper between the M C P and the actions. That feels like a very conservative, safe approach to architecture, which I think resonates with the enterprise crowd. You do not just let the agent talk to the digital wallet or the C R M directly; there is a checkpoint. Gartner is predicting that forty percent of enterprise apps will have these task-specific agents by the end of this year, but I bet the ones that actually succeed are the ones that implement that verification layer. It can reduce A I-related rework by forty percent. If you do not have that gate, you are just waiting for a hallucination to delete your entire customer database.
It is about trust. If you do not have a way for a human to say yes or no to a specific action, you are never going to get the legal department to sign off on a broad deployment. But let us look at the inference layer for a second because there is something interesting there too. Daniel includes the gateway as an optional node. Tools like OpenRouter or Lite L L M. He is basically saying that you should not be hard-coding your agents to a single model. This is something we have preached for a long time.
Which is a very pro-market, pro-competition stance. If G P T four o is down or if Claude three point five Sonnet becomes cheaper or faster, the gateway should just handle that failover automatically. It is that "agent operating system" concept we talked about in episode nine hundred thirty-eight. The infrastructure should be model-agnostic. But Corn, what about the edge? He has edge inference as a node, but with things like Apple Intelligence and Gemini Nano really taking off on-device, does that change the architecture?
It changes the trust boundary. If the inference is happening on my phone or my local workstation, the privacy profile is completely different from a cloud-based agent. Daniel's map treats it as an inference destination, but I think you could argue that edge inference might eventually require its own mini-version of this whole stack. You would have local M C P servers talking to local data. It is a decentralized symphony. Imagine an agent that lives on your laptop, has access to your local files through a local M C P, and only calls out to the cloud for massive reasoning tasks. That is the hybrid future.
Let us get into the meat of this, though: the latent value spaces. This is where I think Daniel's map really shines. He identifies three pathways that most organizations are currently ignoring, and he argues these are where the real long-term value lives. The first one is the move from prompts to a prompt library. The idea is that you do not just write a prompt and use it; you curate it, version it, and store it as an institutional asset.
This is so overlooked. Most people treat prompting like a casual conversation that disappears into the ether. But a really well-engineered system prompt that handles edge cases and maintains a specific brand voice is intellectual property. It is institutional knowledge. If you are not capturing those "golden prompts" in a library, every new agent you build is starting from scratch. You are losing the evolution of your own internal best practices. It is like having a master chef who never writes down their recipes. When they leave, the restaurant is in trouble.
It is like the early days of software engineering before we had version control. Everyone was just overwriting files and hoping for the best. The second latent value space he mentions is the user context loop. This one is fascinating to me because it is different from standard R A G. Instead of just pulling from documents, the system mines previous conversations to understand the user's preferences, their style, and their domain knowledge. It is turning the chat history into a structured memory artifact.
We touched on this in episode eight hundred ten, the agentic interview. The idea that the A I is learning you while you are talking to it. Daniel maps this as a cycle where conversations go to storage, then a separate context-mining workflow processes them, and then they feed back into the vector database. This creates a much more personalized experience. If the agent knows that I prefer concise summaries and that I am currently working on a project about solid-state batteries, it does not need me to explain that every single time. It is building a dynamic user profile that lives outside of any single session. It is the difference between a stranger and a long-term assistant.
And the third one is the output to knowledge management loop. This is the big one for enterprise R O I. Instead of an agent just giving me an answer and that answer staying in my Slack D M, the system identifies high-value outputs and automatically proposes them for the company wiki or the knowledge base. It is the end of the "knowledge silo" where information is trapped in individual threads.
That is how you get to that one hundred seventy-one percent average R O I that Deloitte is talking about. You are not just automating a task; you are automating the creation of company intelligence. If an agent does a deep dive into market trends for a specific product, that research should be available to the whole marketing team, not just the one person who asked the question. But Herman, as much as I like these three, I feel like we could find more. If we are looking at this map as a symphony, there are other sections of the orchestra that should be talking to each other. What did he miss?
Well, I have been thinking about a fourth one: the observability-to-gateway feedback loop. Right now, observability is usually just for the humans. We look at the logs and see that a model failed or that it took too long. But what if that data fed back into the inference gateway in real-time? If the observability layer sees that model X is hallucinating on a specific type of Python code generation, the gateway should automatically reroute those specific queries to model Y. It is a self-healing inference architecture.
That is brilliant. You are basically creating a closed-loop system for quality control. You could even extend that to cost. If the observability layer sees that you are using a high-cost model for a low-complexity task, it could trigger a policy update in the gateway to use a smaller, distilled model like Phi-three or Llama-three instead. You are turning monitoring into active management. It is like having a foreman who reassigns workers based on who is actually getting the job done.
And what about safety? Daniel has safety as a side layer that filters inputs and outputs. But I think there is a latent value space between the safety filter and what I would call red-team intelligence. Every time the safety filter catches a malicious injection or a policy violation, that should not just be a blocked message. It should be a data point that feeds into an automated red-teaming agent that then tests the rest of the system for that same vulnerability.
So the safety layer is not just a shield; it is a sensor. I love that. It turns every attempted attack into a lesson for the entire organization. It is the same principle as the "immune system" approach to cybersecurity. You are using the failures to strengthen the collective defense. If someone tries a "jailbreak" on your customer support agent, your security agent should immediately try that same jailbreak on your internal H R agent to see if it works there too.
We should also talk about the tool registry. Daniel notes that having too many tools in the M C P registry actually degrades performance. It is a signal-to-noise problem for the model. If you give an L L M a list of a hundred tools, it gets confused. So, there is a latent value space between tool usage analytics and registry pruning. If you have ten thousand M C P servers but your agents only ever use fifty of them effectively, the system should automatically "forget" or hide the underperforming tools from the model's primary context.
That is actually a huge technical hurdle right now. The "lost in the middle" phenomenon applies to tool definitions just as much as it does to text. So, having an automated "librarian" agent that manages the M C P registry based on actual performance data would be a massive win for reliability. It is about keeping the agent's "workspace" clean. You do not want your agent tripping over a hammer when it is trying to use a screwdriver.
I want to go back to the neuro-symbolic point for a second. If we look at the Agents layer, maybe a missing latent value space is the grounding-to-fact-check loop. Daniel has grounding as an input, which is correct. It is things like web search or real-time news feeds. But right now, we just kind of dump that grounding data into the prompt and hope the model uses it correctly. What if there was a dedicated verification step where a symbolic agent checks the model's output against the grounding source before the user ever sees it?
Like a real-time auditor. That would solve the "hallucination with data" problem where a model has the right information in its context but still manages to misinterpret it. It is that forty percent reduction in rework again. If the architecture itself guarantees a certain level of factual alignment, the human-in-the-loop becomes much more efficient. They are not checking for basic facts anymore; they are checking for strategic alignment. They are the editor-in-chief, not the fact-checker.
It is funny, the more we talk about this, the more it feels like we are describing a company, not just a software stack. You have the workers, the managers, the auditors, the librarians, and the security team. Daniel's map is essentially an organizational chart for a digital workforce. And if you look at it that way, the "silo problem" is just bad management. Fifty percent of agents are in silos because we are still treating A I like a tool you use, rather than a team member you integrate.
That is a profound shift. If you are an I T leader in twenty twenty-six, your job has moved from "system administrator" to "orchestrator of digital labor." You have to think about how these agents are sharing memory and how they are contributing to the long-term knowledge of the firm. If you just deploy a bunch of disconnected chatbots, you are going to get some productivity gains, but you are not going to get that compounding value. The compounding value comes from those latent value spaces. It comes from the "mined memory" and the "organizational learning" loops. It is about building a brain, not just a bunch of neurons.
Let us talk about the practical side of this. If someone is listening and they are in charge of an A I team, where do they start with this map? It is overwhelming. Fourteen layers and thirty-nine connections is a lot to build out from day one. You cannot just flip a switch and have a symphony.
I think you start at the center and move outward, but you keep the latent spaces in mind from the beginning. You need the model and the inference, obviously. But the very next thing you should do is set up a basic M C P server for your core data and a simple version of the user context loop. Even just saving chat history to a searchable database is a huge step up from what most people are doing. You do not need the full "Agentic Symphony" on day one, but you need an architecture that allows for it. Do not hard-code your prompts. Do not lock yourself into one model provider. Use a gateway. Build with the assumption that the pieces will change.
And I would add: audit your silos. Look at where your agents are currently running. Are they saving their outputs to a place where other agents can find them? If the answer is no, you are leaving money on the table. You are building a "fragmented mind" instead of a "collective intelligence." I think Daniel's map is a wake-up call that we need to stop thinking about "the prompt" and start thinking about "the pipeline." We need to move from "how do I get this A I to answer this question" to "how does this A I's answer improve our entire organization's knowledge."
It is also a call for better standards. M C P is a great start, but we still do not have a great standard for agent-to-agent communication or for cross-organizational memory sharing. If my agent needs to talk to your agent to schedule a meeting or negotiate a contract, how do they do that securely and efficiently? That is the next frontier for twenty twenty-seven, I think. We are building the internal nervous system now, but soon we will need the social protocols for agents.
I am curious about your take on the "vendor prompt" thing again. If we are in this pro-innovation stance, how do we feel about these big companies like OpenAI and Google having so much hidden influence over how our agents behave? Daniel putting it on the map as a distinct layer feels like a way to reclaim some of that sovereignty. It is acknowledging that the "black box" exists so that we can better account for it.
It is about transparency. If I know that a certain model provider has a heavy-handed safety filter that interferes with my specific business use case, I can use the gateway to route those tasks elsewhere. It is about having a multi-model strategy. We should not be reliant on any single "central planner" of A I intelligence. The symphony is better when there are many different instruments and voices. That is the American way, right? Competition, diversity of thought, and decentralization. We want a marketplace of models, not a monopoly on reasoning.
I agree. And I think that is why the open-source layer in the Models section is so important. Llama, Mistral, Qwen, DeepSeek. These models are the "raw materials" that allow companies to build without being entirely beholden to the cloud giants. If you can run a fine-tuned Llama model on-prem, you have total control over that "vendor prompt" layer because you are the vendor. You set the alignment. You set the boundaries. That is true architectural sovereignty.
You are the conductor and the composer. I think that is the ultimate goal of the Agentic Symphony: to give the user or the organization the power to compose their own intelligence. It is a very empowering vision of the future. It is not about A I replacing us; it is about us building these incredibly sophisticated extensions of our own capabilities. We are not just users anymore; we are architects of thought.
So, looking back at the map, is there anything you think is actually a mistake? Something Daniel got wrong?
I might argue that the distinction between "grounding" and "actions" is a bit thinner than he suggests. He says grounding is an input and actions are an output. But in a multi-agent system, one agent's action—like searching the web or querying a database—is another agent's grounding. It is all a matter of perspective. I think as we move toward more complex multi-agent systems, those two layers might eventually merge into a single "external world interface." It is all just I O, right? Input and output.
That is a good point. But I think for the current state of the industry, keeping them separate helps developers understand that they need two different types of logic. Grounding is about gathering truth; actions are about changing the world. Mixing those up can lead to some pretty messy agent behavior, like an agent that accidentally starts deleting files when it was only supposed to be "reading" them to find information. You want that "read-only" boundary for grounding. That is a safety feature in itself.
Fair enough. Safety first, especially when you are giving these things the keys to the digital kingdom. We have covered a lot of ground here. We have looked at the fourteen layers, the thirty-nine connections, the neuro-symbolic critique, and we have even added some of our own latent value spaces like observability-driven routing and safety-driven red-teaming. It feels like the main takeaway is that the "era of the chat bot" is officially over. We are in the "era of the agentic organism" now.
It is a transition from "talking to a computer" to "managing a system." And for our listeners, I think the most important thing is to start visualizing your own A I stack this way. If you cannot draw a map of how your data flows from a user's prompt all the way to your organizational knowledge base, you do not have a strategy; you have a collection of experiments. And experiments are great, but they do not scale.
Well said. I think we should probably wrap this up, but I want to remind everyone that if you want to see this map for yourself, you can find it on Daniel's GitHub or search for "The Agentic Symphony" on Hugging Face. It is an interactive tool, so you can actually click through the layers and see the connections we have been talking about. It is a great way to spend an afternoon if you are an A I nerd like us.
We really do love this stuff. This has been episode one thousand eighty-two of My Weird Prompts. We have talked about the shift from silos to organisms, the power of M C P as a nervous system, and why your prompt library might be your most valuable asset by the end of this year.
Thanks to Daniel for the prompt and for all the hard work on this visualization. It gave us a lot to chew on today. If you want to dive into our archives, we have over a thousand episodes now at myweirdprompts dot com. You can find the R S S feed there, and if you are on Telegram, just search for My Weird Prompts to get notified whenever a new episode drops.
We will be back next time with more explorations of the weird and wonderful world of human-A I collaboration. Until then, I am Herman.
And I am Herman Poppleberry. Thanks for listening.
Take care, everyone.
See you in the next one.
And remember, keep those prompts weird. It is where the best ideas come from.
The edge cases are where the future is written.
Alright, that is a wrap on the symphony for today.
Peace.
