Agentic AI: what are the top startups now?

Is agentic AI finally becoming a real business?

Agentic AI is now producing some of the fastest-growing software startups, although most companies are still far from using autonomous agents across their businesses.

McKinsey's 2026 State of AI survey found that 40% of organizations with more than $1 billion in annual revenue were scaling AI agents in at least one business function, up from 27% the previous year. Among smaller organizations, adoption remained flat at 22%. Large companies are clearly moving faster, even though they face more complicated security, procurement, and integration requirements.

The revenue figures are becoming difficult to ignore. Cognition reached approximately $1 billion in annualized revenue in September 2026, according to Bloomberg. Glean surpassed $300 million in annual recurring revenue in May, while Sierra reached an estimated $200 million ARR. Sacra estimates that legal AI startup Harvey crossed $400 million ARR in August. Revenue like this puts a company near the top, and we cover who the top companies are today in our agentic AI report.

Still, these companies represent the successful end of a much wider market. McKinsey found that roughly two in ten organizations were scaling agents across their businesses, compared with 47% scaling chatbots. Buying an AI assistant remains considerably easier than trusting software to execute business processes.

The most successful agentic AI startups have already moved into workflows where customers can measure completed work: software development, customer service, legal research, and enterprise operations. General-purpose agents are attracting attention too, but their commercial results remain less consistent.

Which agentic AI startups are actually leading today?

Cognition, Harvey, Glean, Sierra, Decagon, and LangChain are currently among the strongest agentic AI startups, although each leads a different part of the market.

Cognition has built a major business around AI software engineering. Harvey has become a leading legal AI platform, with a customer base extending across major law firms and corporate legal departments. Sierra and Decagon compete directly in customer-service automation, while Glean helps companies connect internal knowledge to AI-powered workflows.

LangChain takes a different approach, providing infrastructure that developers use to build, evaluate, and operate agents. Its success depends on companies continuing to create their own applications rather than buying everything from specialist vendors.

We also include Replit, Parloa, Hippocratic AI, Legora, and Manus among the most important names to watch. Replit serves a broader audience of software creators, Parloa specializes in voice-based customer service, Hippocratic AI operates in healthcare, and Legora competes with Harvey in legal AI. Manus is one of the best-known general-purpose agents.

For this ranking, an AI agent means software capable of carrying out a sequence of tasks, using tools or business systems, and making at least some decisions along the way. Products that only generate text are excluded. We also distinguish between companies selling finished agents and those providing infrastructure for building them.

The financial figures below combine company disclosures, reporting from Bloomberg and Reuters, and estimates from Sacra. Annualized revenue and contracted recurring revenue are not always calculated the same way.

Startup	Main market	Latest revenue evidence	Valuation reference
Cognition	AI software engineering	~$1B annualized revenue	$48B
Harvey	Legal AI workflows	~$400M estimated ARR	$15.5B
Glean	Enterprise search and AI agents	$300M ARR	$7.2B
Sierra	Customer-service agents	~$200M estimated ARR	$15.8B
Decagon	Customer-service agents	~$100M estimated annualized revenue	$4.5B
LangChain	Agent development infrastructure	7,000+ LangSmith teams/customers reported	Private
Replit	Autonomous app development	$150M annualized revenue reported in 2025, with subsequent growth	$9B
Parloa	Enterprise voice agents	$50M+ ARR	$3B
Hippocratic AI	Healthcare voice agents	10M+ patient calls reported	Multibillion-dollar private valuation
Legora	Legal AI workflows	Strong enterprise growth; limited comparable public revenue data	Multibillion-dollar private valuation
Manus	General-purpose AI agents	Nearly $500M annualized revenue reportedly reached in June 2026	Former $2B–$3B acquisition reference

Is Cognition the biggest winner in agentic AI?

Cognition is currently the strongest agentic AI startup by reported annualized revenue among the specialist companies we examined, although its business is broader than autonomous coding alone.

The company develops Devin, an AI software engineer capable of working with code repositories, fixing bugs, running tests, and completing development tasks. Cognition also acquired Windsurf's business in 2025, bringing a large existing coding-product business into the company.

In September 2026, Cognition raised $2 billion at a $48 billion valuation. Its annualized revenue had already climbed from $492 million in May to approximately $900 million in early September, an 83% increase in roughly four months. Bloomberg subsequently reported that the company had reached a $1 billion annualized revenue run rate later that month.

That means Cognition's reported revenue run rate roughly doubled in four months. Customers include Nvidia, Citi, Mercedes-Benz, BNY, and GE Aerospace.

However, Devin's standalone business was much smaller before the Windsurf acquisition. Sacra estimates that Devin reached approximately $73 million ARR in June 2025. Much of Cognition's later scale therefore reflects the combined coding platform, including products that work more like advanced development assistants.

The financial burden is substantial. The Information reported that expensive computing infrastructure could push Cognition's annual cash burn toward $800 million. At a $48 billion valuation, the company is valued at approximately 48 times its latest reported annualized revenue.

Why is Sierra growing so fast in customer service?

Sierra is growing quickly because large companies are already paying substantial amounts to automate customer conversations, and its existing customers are expanding their use of AI agents.

According to Sacra, Sierra reached approximately $200 million ARR in May 2026, compared with $130 million at the end of 2025 and $26 million at the end of 2024. That is nearly eightfold growth from its 2024 baseline.

The company sells AI agents that answer customer questions, handle account-related requests, and complete supported actions across text and voice channels. Customers pay through usage-based or outcome-based arrangements, including contracts tied to conversations or successful resolutions.

This gives Sierra access to spending that already exists. Large companies spend heavily on contact centers, outsourcing, and customer-support platforms. Automating even part of that work can justify meaningful software contracts.

Voice is becoming increasingly important to Sierra's business. According to Sacra, voice interactions had overtaken text as its primary interaction channel by October 2025, just as the company was expanding into more complex enterprise deployments.

Investors have responded aggressively. Sierra raised $950 million at a $15.8 billion valuation in May 2026, compared with a $4.5 billion valuation in October 2024. Its valuation increased approximately 3.5 times in 19 months.

At roughly 79 times estimated ARR, Sierra's valuation assumes that customer-service automation will become a much larger business. Its rapid revenue growth makes that possible, but the company still has to prove that automating millions of conversations can produce attractive margins.

Can Decagon actually catch Sierra?

Decagon is now Sierra's most credible specialized challenger in customer-service AI, although Sierra remains roughly twice as large by the latest reported revenue estimates.

Decagon raised $250 million in January 2026 at a $4.5 billion valuation. The company reported adding more than 100 global enterprise customers during the preceding fiscal year, including Avis Budget Group, Block, and Deutsche Telekom.

Sacra estimates that Decagon reached $100 million in annualized revenue in July 2026, up from $44 million at the end of 2025. That represents approximately 127% growth in seven months.

Sierra's estimated $200 million ARR puts it ahead in reported revenue, but the valuation gap is even larger. Sierra's $15.8 billion valuation is 3.5 times Decagon's $4.5 billion valuation.

Decagon has reached roughly half Sierra's reported revenue while carrying less than one-third of its valuation. The figures come from different reporting dates, but the gap is large enough to be meaningful.

Decagon also faces competition beyond Sierra. Salesforce Agentforce, Zendesk, and Intercom's Fin are all trying to automate customer service inside software that many companies already use. With Salesforce, Zendesk and Intercom in the same race, we look at who is ahead and who is falling behind in our agentic AI market report.

Is Harvey becoming the biggest name in legal AI agents?

Harvey is currently the leading legal AI startup by the commercial scale we can verify, and its position has strengthened considerably over the past year.

In September 2026, Harvey raised $550 million at a $15.5 billion valuation, only six months after raising money at an $11 billion valuation. The company has almost doubled its valuation from the $8 billion level reached in December 2025.

Revenue has followed a similar trajectory. Sacra estimates that Harvey crossed $400 million ARR in August 2026, compared with approximately $195 million at the end of 2025. The Information separately reported more than $350 million in annualized revenue in August, up from roughly $190 million in January.

Harvey's latest funding announcement also stated that 80% of Am Law 100 firms now use its products. Earlier company and market disclosures described more than 142,000 lawyers using the platform across over 1,500 customers in 60 countries.

Those figures give Harvey a significant distribution advantage. Large law firms can expand usage across departments and offices once their internal security and compliance teams approve a platform.

The company is also developing its own specialized models. In 2026, Harvey introduced Harvey Tenet, a post-trained open-weight model designed for legal work, alongside Harvey LAB, a benchmark for legal agents.

Legora remains an important rival, especially among European law firms. But Harvey's revenue, customer penetration, and expansion into legal-specific models make it the stronger documented commercial leader for now.

Has Glean become more than an enterprise search tool?

Glean has successfully expanded into enterprise AI agents, although much of its $300 million ARR still comes from its broader enterprise search and knowledge platform.

In May 2026, Glean announced that it had reached $300 million ARR, just 15 months after crossing $100 million. That represents a threefold increase and approximately 120% annualized growth over the period.

Glean also reported that its number of Fortune 500 customers had nearly doubled year over year. More than 85% of its customers were deploying the platform across at least five departments.

This is especially interesting because enterprise AI search has become much more competitive. Microsoft, Google, and Salesforce already offer AI capabilities inside their existing software suites, yet Glean has continued growing.

Its agents can draw on information across connected company applications, respecting the permissions attached to that data. For example, an employee might need information from Salesforce, Slack, Google Drive, and internal documentation to complete a single task.

Glean's cross-platform approach works particularly well for organizations whose information is scattered across different systems.

There is an important limit to the revenue comparison, though. Glean's $300 million ARR includes enterprise search and assistant products. We cannot establish how much customers are specifically paying for autonomous agent execution.

Is LangChain the most important startup building AI agents?

LangChain is currently one of the most important agentic AI infrastructure companies because developers use its tools to build agents for thousands of different applications.

The company offers LangChain and LangGraph as development frameworks, alongside LangSmith, a commercial platform for testing, monitoring, and deploying agent applications.

LangChain has reported more than 350 million monthly open-source downloads and over 7,000 teams or customers using LangSmith. Its technology has also been adopted by five of the Fortune 10 companies.

The download figure needs careful interpretation. A large number of downloads can come from automated build systems, so it does not mean 350 million individual developers. The commercial adoption figures are more informative.

LangChain's customers include Salesforce, Toyota, Cisco, Morningstar, and C.H. Robinson. In a published customer case study, C.H. Robinson described automating approximately 5,500 orders per day, saving more than 600 working hours daily.

LangChain's main competitors include cloud platforms and model providers offering their own development tools. Yet its position remains interesting: it can benefit from companies building agents internally, regardless of which customer-service, legal, or coding startup eventually becomes dominant.

Are Cognition and Replit replacing traditional software development?

Cognition and Replit are making AI-generated software commercially mainstream, although neither company has demonstrated that human software engineers can be broadly replaced without supervision.

Their audiences are different. Cognition's Devin is designed for professional engineering work, while Replit Agent helps users build applications through natural-language instructions, including people with limited programming experience.

Replit reported in September 2025 that annualized revenue had increased from $2.8 million to $150 million in less than a year. That represents more than 50 times growth. The company subsequently raised $400 million at a $9 billion valuation in March 2026. A $9 billion valuation comes after fifty-fold revenue growth; we compare what companies were worth then and now in our report on agentic AI.

Cognition's more recent revenue growth is even larger in absolute terms, though its Windsurf business includes coding assistance alongside autonomous engineering.

There is evidence that these tools are changing software budgets. McKinsey's 2026 survey found that 32% of organizations had decided against purchasing at least one software product or feature because they could build it internally using agentic coding tools.

The question is how much complicated development work agents can handle reliably. Creating a prototype or fixing a defined issue is different from maintaining a large production system, managing security risks, and making architecture decisions.

Are voice AI agents becoming a big startup market?

Voice AI agents are now a serious business, particularly for companies handling large volumes of telephone calls, appointments, and customer-service requests.

Parloa is one of the clearest examples. The German-founded company surpassed $50 million ARR in 2025 and raised $350 million at a $3 billion valuation in January 2026.

Parloa also reported 150% net revenue retention. That means a group of existing customers increased its spending by roughly 50% overall, including the effects of customer losses and contractions. It is a strong indication that customers are using more of the platform after buying it.

Retell AI, Bland AI, and Vapi address the voice-agent market from different angles. Retell and Vapi provide infrastructure for developers to build programmable phone agents, while Bland focuses on automated business conversations. PolyAI and Cresta are also established competitors in contact-center AI.

Voice automation has more complicated operating requirements than many text-based workflows. Agents must handle interruptions, accents, background noise, caller authentication, and escalation to human employees.

The commercial opportunity is substantial because companies already spend money answering calls. What remains less clear is which startups will capture the most value: those selling complete voice-agent systems or those supplying the infrastructure underneath them.

Startup	Main focus	Commercial evidence	Market position
Sierra	Enterprise customer-service agents	~$200M estimated ARR	Leading large-scale specialist
Decagon	Customer-support automation	~$100M estimated annualized revenue	Fast-growing enterprise challenger
Retell AI	Voice-agent infrastructure	~$60M+ estimated annualized revenue	Developer-focused voice platform
Parloa	Enterprise contact-center agents	$50M+ disclosed ARR	Strong enterprise voice specialist
PolyAI	Conversational voice automation	Major contact-center deployments	Established enterprise competitor
Cresta	Contact-center AI	Established enterprise customer base	Combines human assistance and automation
Bland AI	Automated telephone workflows	Enterprise deployments	Voice-first automation platform
Vapi	Voice-agent development APIs	Developer adoption; limited public revenue data	Infrastructure specialist

Can Hippocratic AI make healthcare agents a real business?

Hippocratic AI has shown that healthcare voice agents can operate at meaningful scale, although its strongest use cases remain structured patient interactions rather than independent medical decisions.

The company develops conversational agents for patient outreach, education, follow-up, and care coordination. These activities involve large amounts of repetitive work that healthcare providers frequently struggle to complete at sufficient scale.

In March 2026, Becker's Hospital Review reported that Advocate Health had used Hippocratic AI to contact more than 15,000 hypertension patients in 12 days. The system helped identify patients with normal blood pressure and escalate cases requiring additional attention.

Hippocratic AI has also reported completing more than 10 million patient calls. That figure demonstrates substantial usage, although call volume alone does not establish clinical effectiveness.

The company publishes safety-performance metrics, but those assessments depend heavily on its own evaluation methods. We should distinguish them from independent evidence that autonomous AI can safely make unrestricted medical decisions.

Other healthcare AI startups, including Abridge and Ambience Healthcare, have grown by helping clinicians with documentation and related administrative tasks. Those products are often less autonomous, but they address major sources of work inside hospitals. Less autonomous tools can still build big businesses; we compare which business models are winning in our agentic AI report.

What happened to Manus, the general-purpose AI agent?

Manus remains one of the most commercially interesting general-purpose AI agent startups, but its reversed Meta acquisition has made its current position unusually complicated.

Manus attracted attention by offering agents capable of conducting research, navigating websites, and completing multi-step tasks through virtual computers.

The company reportedly surpassed $100 million ARR within approximately eight months of launching. The Information subsequently reported that its annualized revenue rate had climbed to nearly $500 million by June 2026, roughly four times the level before its Meta acquisition.

Meta had agreed to acquire Manus in December 2025 for a reported $2 billion to $3 billion. However, Chinese regulators ordered the transaction to be unwound in April 2026.

Reuters confirmed in August that Manus would return to operating independently. Some user data created during its period under Meta ownership also had to be deleted to comply with regulatory requirements.

Manus has since reportedly secured more than $500 million in new financing in October 2026, giving the newly independent company additional resources to continue expanding.

That history makes Manus difficult to rank. Its reported revenue growth suggests substantial demand for general-purpose agents, but the separation introduces questions about customer continuity, ownership, and future funding.

Compared with startups automating a single profession, general-purpose agents also have to perform consistently across many unrelated tasks. A system that handles market research, spreadsheets, online services, and browser workflows must deal with a much wider range of possible failures.

Are specialized AI agents winning against general-purpose agents?

Specialized agentic AI startups currently have the stronger track record of repeatable commercial success, especially in coding, customer service, and legal work.

Cognition's software agents, Harvey's legal workflows, and Sierra's customer-service products all serve existing business needs. Customers can evaluate them through software tasks completed, documents reviewed, or support requests resolved.

General-purpose platforms such as Manus, Lindy, and Relevance AI offer greater flexibility. They can help users create workflows across many tools without requiring a separate specialist product for every task.

That flexibility has genuine value, particularly for smaller businesses. However, broad automation also requires more integrations, permissions, testing, and monitoring.

This is one reason companies such as Browserbase, CrewAI, and LangChain deserve attention. They provide infrastructure that helps developers operate agents across different tools and environments.

The market is currently rewarding startups that solve one expensive problem extremely well. General-purpose platforms may eventually serve much larger markets, but specialized workflows have produced more consistent evidence of enterprise spending so far.

Are agentic AI startups attracting too much money?

Some of the biggest agentic AI startups now carry valuations that look very expensive relative to their revenue, even after accounting for exceptional growth.

Consider Cognition, Sierra, Harvey, and Decagon. All four have demonstrated substantial revenue, but their latest private valuations imply that investors expect much larger businesses in the coming years. Valuations that assume far bigger businesses are big bets, and we follow which investors are making the biggest ones in our agentic AI market report.

Cognition's $48 billion valuation is approximately 48 times its latest reported $1 billion annualized revenue run rate. Sierra's $15.8 billion valuation is about 79 times Sacra's estimated $200 million ARR. Harvey's $15.5 billion valuation represents approximately 39 times its estimated $400 million ARR.

Decagon's comparable ratio is about 45 times annualized revenue.

These are approximate figures, using disclosures and estimates from different dates. They remain useful for understanding what investors are paying for.

Another interesting comparison is Sierra versus Decagon. Sierra has around twice Decagon's reported revenue but is valued at roughly 3.5 times as much. Investors are assigning Sierra a substantial premium for its market position, expected growth, and ability to expand customer contracts.

Cognition presents a different challenge. Its revenue growth has been extraordinarily fast, but The Information's reported computing costs show how expensive building and operating advanced coding AI can become.

Funding concentration adds another layer. A September 2026 dataset published by New Market Pitch covered 99 agentic AI startups with approximately $25.3 billion in cumulative funding. The top 10 accounted for roughly 57% of that amount, while the top 20 represented about 75%.

That implies approximately $14.4 billion concentrated in just 10 startups, with the remaining $10.9 billion distributed among 89 companies.

Can agentic AI startups survive competition from OpenAI and Anthropic?

The leading agentic AI startups can survive competition from OpenAI and Anthropic, but companies offering little beyond a convenient interface around a general-purpose model face a much harder future.

OpenAI and Anthropic continue improving their coding and agent capabilities. Salesforce offers Agentforce inside its customer-management platform, while Microsoft and Google can distribute AI tools through applications companies already use.

These larger players have enormous advantages in computing resources, existing customer relationships, and product distribution.

Specialized startups are trying to build advantages elsewhere. Harvey offers legal workflows, professional controls, and increasingly its own legal models. Sierra integrates with customer-service systems and business processes. Glean provides connections across enterprise applications and their associated permissions.

LangChain approaches the problem from the infrastructure side, allowing developers to combine models and tools rather than relying on a single provider.

Startups with deep integrations, specialized customer knowledge, and difficult-to-reproduce workflows have a credible path forward. Basic agent interfaces are easier for model providers and established software companies to absorb.

Which smaller agentic AI startups could become major competitors?

Relevance AI, Lindy, CrewAI, Browserbase, Hebbia, Ema, and 11x are among the smaller agentic AI startups worth watching, although their commercial evidence is generally less complete than that of the market leaders.

Relevance AI and Lindy help businesses create agents for recurring tasks without requiring extensive software development. They are competing for customers who want automation but do not have dedicated engineering teams.

CrewAI provides frameworks for coordinating multiple agents, while Browserbase supplies browser infrastructure that agents can use to interact with websites. Both could benefit from growth across many different agent applications.

Hebbia focuses on document-heavy research, including workflows used in finance and other professional industries. Ema develops configurable enterprise AI employees, while 11x applies AI agents to sales development and outbound prospecting.

These startups represent several possible directions for the market. Some sell complete applications, others help customers build agents, and a third group provides the technical infrastructure required to run them.

Their main weakness as ranking candidates is the lack of consistently comparable financial information. Product launches, funding rounds, and customer announcements tell us less than recurring revenue, retention, and actual workload completed.

What are the top agentic AI startups now?

Cognition, Harvey, Sierra, Glean, Decagon, and LangChain are our strongest agentic AI startup picks today, with Replit, Parloa, Hippocratic AI, Legora, and Manus also deserving a place among the important companies in the market.

Cognition stands out for commercial scale. Its reported annualized revenue has reached approximately $1 billion, although that includes a broader coding business following the Windsurf acquisition.

Harvey is currently our strongest legal AI pick, supported by substantial recurring revenue, adoption among major law firms, and a growing collection of legal-specific products. Sierra leads our customer-service shortlist, while Decagon has become a credible alternative with a smaller valuation relative to revenue.

Glean is especially well positioned among enterprises that need agents to work across multiple applications. LangChain has a different advantage: developers can use its tools to build agents for almost any industry.

Commercial success remains heavily concentrated. The biggest revenue figures come from startups automating software development, customer service, and legal work. General-purpose agents have shown impressive growth too, particularly Manus, but their longer-term economics and reliability are less well established.

If we had to choose one overall leader, Cognition currently has the strongest claim on reported revenue scale among the specialists examined. For specialized enterprise agents, Harvey and Sierra are particularly convincing.