For two years, the enterprise conversation about AI was organized around a single question: What can these models do? You benchmarked them, piloted them, marveled at them. That was the right question for the experimentation phase, but it is the wrong question today.
We’ve now entered the agentic enterprise phase, where AI doesn't just answer questions but executes work: monitoring change, preparing deliverables, participating in the decisions that move the business. Once AI is doing the work, the strategic question changes from "what can this model do?" to "what does my company learn every time it makes a decision and who owns that learning?"
Our CEO, Jack Kokko, likes to say a company's enterprise value is the sum of all the decisions it makes. If value accrues one high-quality decision at a time, the most valuable asset a company can build in the age of AI is not a model. It is the loop that lets the organization learn from every decision: the context behind it, the evidence that informed it, the outcome that followed, and the pattern that connects them. Own that loop, and the company compounds, with every decision sharpening the next.
The Advantage of Being Multi-Model
The instinct to consolidate models is understandable: pick a leading model provider, standardize on it, and build everything on top. It feels like a clean platform decision.
But it's a mistake and not just for the reason people usually give.
The usual objection is lock-in and pricing leverage. Those reasons are real, but secondary. A deeper problem is the jagged frontier. If the loop is the asset you own, the models underneath should be the engines you rent. At AlphaSense, we benchmark models continuously — with every major model release, every feature upgrade, month after month — against real-world tasks. Not only do different providers regularly leapfrog each other, but different models are dramatically better at different tasks.
In our context that means some models are good at data extraction, others at financial modeling, and still others at slide composition or long-form writing. No model is great at search, which is why specialized AI agents play a critical role. A model-agnostic architecture that assumes a dynamic ecosystem of models and agents is one that will age well.
The market has recently raised another problem worth paying attention to: one of incentives and where your learning goes. Satya Nadella calls it the Reverse Information Paradox: “In the age of AI, the buyer risks giving away knowledge, just in order to use what they bought.” Model providers are in the business of building the best general-purpose models in the world, and they do it by learning from usage. Route your firm's proprietary decisions, data, and workflows through a single provider, and you risk handing over the record of how your company thinks to a vendor whose incentive is to generalize it. Your edge becomes an input to a commodity your competitors can also buy.
Winning architecture has three parts, and the firm should own all three: First, an agent harness, the proprietary code that wraps the model and turns a raw LLM into an active, autonomous agent that executes multi-step workflows, tuned to how the business actually works. Second, a learning loop that captures decisions as proprietary memory. Third, a routing layer that sends each task to whichever model or agent is best for it right now. In that design, models are interchangeable and constantly improving. The harness and loop are both yours, and they grow more valuable with every decision.
You Don’t Need To Build Every Agent
The work of a business spans many domains including strategy, business development, sales, HR, legal, and finance. Each will be best served by a specialized agent that is excellent at that one thing. Owning your harness, loop, and routing does not mean building every agent that runs inside them. The right pattern is a harness orchestrating a roster of internal and specialized third-party agents.
And integration with your harness is only half of how a specialized agent adds value. The other half is a purpose-built interface for the people who live in that function every day. Some work is done best inside a surface designed around how a function actually operates — the way an analyst, a recruiter, a banker, or a seller thinks and moves through a task — rather than routed through a general-purpose agent. The strongest specialized agents give you both, and your teams should reach for whichever fits the task.
So the real work for enterprise leaders is twofold: build the harness and the loop it feeds, then identify the winners among specialized agents and integrate them. Ownership where it compounds; partnership where it doesn't.
What Separates a Winning Specialized Agent from a Wrapper
This raises the next question: how do you tell a durable specialized agent from a thin wrapper or a database with a connector bolted on?
The reason your firm’s harness and learning loop are worth owning is that tremendous value accrues from being wired deeply into your proprietary data and record of decisions. An agent is only as good as what it can find and how well it understands what it finds. A specialized agent has to clear the same bar.
The durable advantage is proprietary data plus a harness purpose-built to retrieve and reason over it. Automation on top of commodity information and third-party MCPs (model context protocol) is a thin layer that looks impressive in a demo and falls flat under real conditions. A specialized agent built on control of unique content, retrieval trained on that content, and reasoning tuned to the domain's specific questions will beat the alternative every time.
There is a mirror-image failure that is easier to miss. Some providers have the proprietary data but treat a connector as the finish line: expose the database through an MCP, call it an AI strategy. It isn't. MCP is a useful standard, and access to good data matters, but access is not intelligence. A connector hands a model an endpoint to query. It does not give it a retrieval system that connects dots across sources, a way to manage context through a long task, or the reasoning to carry evidence across a multi-step workflow.
In the specialized-agent race, the winners bring three critical pieces: proprietary data, a purpose-built agent that reasons over it, and a purpose-built interface optimized for both. A wrapper has an agent but not the data. A connector-only provider has the data but not the agent. Neither half is enough.
The Hidden Cost of Building Every Agent
Market intelligence is the domain I know best, so let me be concrete about what building it internally actually costs.
To stand up an institutional-grade market intelligence agent, a firm has to license and aggregate a sprawling set of external sources — filings, transcripts, broker research, news, expert interviews, private market data — and then do the unglamorous work of cleaning it, resolving entities and relationships across it, keeping it current, managing the rights around it, and building retrieval that knows which source to trust for which task. That is not a project. It is a business and a different business than the one most enterprises are in.
Again, the tempting shortcut is to skip the aggregation work by licensing what you can through MCPs. But a stack of connectors does not add up to a decision-grade agent. It relocates the problem. Each MCP arrives as its own silo, with its own query semantics and its own view of the world. The cross-source work still lands on your team: resolving entities across feeds, knowing which sources to trust for which tasks, managing context as raw documents flood in, verifying what comes back. So do the accuracy gaps and exploding token and licensing costs that come with it.
Every quarter spent on this is a quarter not spent on the north star: your own enterprise data, your own decision loop, your own orchestration layer. It is effort spent rebuilding infrastructure that specialized providers have spent years perfecting.
This is where AlphaSense fits. We have spent more than a decade assembling the proprietary content, knowledge and context graphs, institutional-grade tools and retrieval systems that market intelligence demands. And with SuperAnalyst, we’ve built an agent harness optimized around those tools and data and the decisions they inform.
Investors, executives and advisors work with their own SuperAnalyst directly in our platform, inside an interface built around how their work actually gets done and the same capabilities are available programmatically, so SuperAnalyst intelligence powers the bespoke applications and workflows a firm builds around its own enterprise harness.
This is not the buy-vs-build pitch you were bracing for. It’s the opposite. Build your harness. Own your loop. Route across the best models. And for the specialized domains where the differentiation is proprietary data and years of purpose-built infrastructure, buy the agents built by teams focused entirely on getting them right.
What the Winners Will Understand
The firms that win the agentic era will be the ones that understand early that enterprise value is the accumulation of decisions and who can architect accordingly. They will own the loop that makes them smarter with every decision, and they will surround it with the best specialized agents in the market rather than trying to build them all alone.
Connected is not the same as intelligent. In the agentic enterprise, intelligent means owning what compounds and partnering for the rest.
