Sierra raised $950m at a $15bn valuation in May 2026, and it is not even the most expensive agent company on the list below. Cognition sits at $26bn. Harvey took $550m at $15.5bn in September 2026. Capital on that scale buys engineering depth, but it also creates a trap for buyers, because a funding round is not evidence that a vendor can deliver your integration. Gartner reckons that of the thousands of vendors now claiming agentic AI capability, only about 130 are real. The distance between that valuation and a working deployment is where procurement budgets go missing, which is why Crunch-IS is a leader in custom AI agent development services, building around the integration and evaluation work that demos leave out. Below is how the American market actually divides, and six companies operating in it.
Most of the money is going to the wrong half of the business
MIT’s Project NANDA studied 300 public AI deployments, surveyed 153 leaders and interviewed 52 executives for its 2025 report. Against $30–40bn of enterprise spending, 95% of the generative AI pilots it examined produced no measurable effect on profit and loss.
The reason is not model quality. MIT found more than half of enterprise AI budget went into sales and marketing tools, while back-office automation, the category with the strongest measured returns, stayed underfunded. Agents that draft marketing copy are easy to fund and easy to demo. Agents that reconcile invoices are neither, and they are where the money is.
Gartner’s forecast points the same way: more than 40% of agentic AI projects cancelled by the end of 2027, on escalating costs, unclear business value or inadequate risk controls. Note what is missing from that list. Nobody cancels because the model was not clever enough.
Half the market is still hedging
A Gartner poll of 3,412 webinar attendees in January 2025 found 19% of organisations had made significant investments in agentic AI and 42% only conservative ones. Another 31% were waiting to see, and 8% had spent nothing at all.
That distribution matters when you are choosing a partner. A vendor whose reference customers are all in the 19% has been solving problems at a scale and budget most buyers will not have. A vendor who has only served the cautious 42% may never have run an agent past a few thousand transactions a month. Ask which group the references came from.
Platform, product or build
American vendors fall into three groups, and confusing them is the most common procurement error.
- Agent platforms. You bring the use case, they bring the runtime, orchestration and guardrails. Fast to start, and you inherit their architecture.
- Vertical agent products. The workflow is already modelled for law, support or coding. Very fast if your process matches theirs, expensive to bend if it does not.
- Development firms. They build the agent against your systems and leave the code with you. Slower to first demo, and the only option when the workflow is the differentiator.
A company with $15bn behind it will sell you the first two. Neither is wrong. They are simply answers to a question many buyers have not asked yet.
Top AI agent development companies in USA
These six run agents in production for named customers. They are grouped by what they actually build rather than ranked, because a coding agent vendor and a customer-operations platform are not competing for the same budget.
Crunch-IS
Builds agents as bespoke development work rather than licensing a platform, leaving the agent, its prompts and its evaluation harness inside the client’s own codebase. Integration, controls and handover are treated as the deliverable. Suits organisations whose workflow is the competitive advantage and cannot be reshaped to fit someone else’s product.
Sierra
Founded in San Francisco in 2023 and positioned as an agent operating layer rather than a chatbot tool: goals, guardrails, backend connections. It reports serving roughly 40% of the Fortune 50, with customers including SiriusXM, Sonos, Chime, Nordstrom and Rivian. Relevant where customer-facing conversation volume is the core problem.
Cognition
Best known for Devin, an autonomous software engineer now deployed at Mercedes-Benz, Goldman Sachs, the US Navy and Itaú. The company says Devin writes 89% of Cognition’s own code, which is an unusually testable claim for this market. Of interest to engineering organisations rather than business units.
Decagon
Around 210 people, built on the premise that agents can absorb most tier-one customer support. It added more than 100 enterprise customers during 2025 across travel, financial services, healthcare and retail, on an estimated $35m of annual revenue at a $4.5bn valuation. A narrow bet, executed narrowly.
Harvey
San Francisco, focused entirely on legal work, with more than 3,000 paying organisations and over $400m in annual recurring revenue. It raised $550m in September 2026 at a $15.5bn valuation and has opened offices from Dublin to Singapore. Worth evaluating only if the use case genuinely sits in legal process.
LangChain
Provides the open-source orchestration framework a large share of custom agents are built on, and raised $125m at a $1.25bn valuation. It is infrastructure rather than a finished agent, which matters: choosing it is a commitment your development team will live with for years, including its upgrade path. Relevant when you are building in-house and need the plumbing rather than the product.
Two American habits worth resisting
The first is buying the platform before defining the workflow. It feels like progress because something is running by week three, and it postpones the only hard question, which is what the agent is allowed to decide on its own. Teams that answer that question late rebuild.
The second is treating an agent as a product launch rather than an operational system. Launches end. Agents need someone watching cost per task, drift in outputs and the rate at which humans override them, every month, for as long as the thing runs. Budget for that role or accept that nobody holds it.
What the valuations do not tell you
The American agent market is the best-capitalised software category in a decade, and capitalisation says nothing about whether a vendor can read your claims system. The useful due-diligence question is not how much a company has raised, but what it has in production, for whom, and what broke in the first month. Firms that have shipped answer that quickly. Firms selling the demo change the subject.
