We mined the CB Insights database to map 170+ AI agent startups across 26 categories. We also provide an outlook on AI agents’ progress, limitations, and future directions.
“Digital coworkers” are moving from concept to reality.
While AI copilots have already made inroads across industries, the next evolution — autonomous agents with greater decision-making scope — is arriving quickly. AI agent startups raised $3.8B in 2024 (nearly tripling 2023’s total), and every big tech player is already developing AI agents or offering the tooling for them.
Implications for enterprises will be far-reaching, from altering workforce composition (with new hybrid teams of humans and AI agents) to maximizing operational efficiency through full automation of routine tasks.
Below we identify 170+ promising startups developing AI agent infrastructure and applications.
CBI customers: Track 250+ AI agent companies
We selected companies for inclusion based on Mosaic health scores (500+) and/or funding recency (since 2022). We included private companies only and organized them according to their primary focus. This market map is not exhaustive of the space.
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Outlook on AI agents
Fully autonomous agents remain limited due to issues pertaining to reliability, reasoning, and access. Most agent applications today operate with “guardrails” — within a constrained architecture where, for example, the LLM-based system follows a decision tree to complete tasks.
Agents featured on this map include some combination of the following components:
Reasoning: Foundation models that enable complex reasoning, language understanding, and decision-making. These models evaluate information and form the cognitive core of the agent.
Memory: Systems that store, organize, and retrieve both short-term contextual information and long-term knowledge.
Tool use: Integration capabilities that allow agents to interact with external applications, APIs, databases, the internet, and other software.
Planning: The agent’s architecture for breaking down complex tasks into more manageable steps, reflecting on performance, and adapting as necessary.
We expect more startups to move up the scale of autonomy as AI capabilities advance. Improvements in reasoning and memory will enable more sophisticated decision-making, adaptability, and task execution.
For example, in September 2024, legal AI startup Harvey announced that OpenAI’s o1 reasoning model, supplemented with domain-specific knowledge and data, was enabling it to build legal agents. The company, which raised $300M at a $3B valuation in February 2025, has doubled its sales force in the last 6 months, indicating rising market demand.
While the above market map highlights the private landscape (with a focus on enterprise applications), tech giants and incumbents are also launching agents. We predict big tech and leading LLM developers will own general-purpose AI agents, but there are many opportunities for smaller, specialized players.
Looking ahead, watch for new form factors outside of the copilot/chatbot interface that will push the boundaries of what an “agent” is. Early indications of this include “AI-native” workspaces — tools and platforms built from the ground up around AI capabilities, rather than layering AI features on top of a traditional product. For instance:
Eve’s legal platform aims to automate aspects of the whole case lifecycle (from case intake to drafting).
Hebbia’s Matrix product builds spreadsheets that mine information from files (in rows) and deliver answers to questions (in columns), proactively discovering, organizing, and surfacing data.
With its Dia product, The Browser Company is exploring web browsing interfaces that can summarize content, automate repetitive web tasks, and even anticipate next actions.
Category overview
AI agent infrastructure
This segment covers companies building agent-specific infrastructure. (We excluded general genAI infrastructure markets like foundation models and vector databases from the map.)
Development tools
A diverse ecosystem of tools has emerged to support agents’ development. These range from memory frameworks like Letta that enable persistent, retrievable memory across interactions; to tools that allow agents to take action via integration (e.g., Composio), authentication (e.g., Anon), and browser automation (e.g., Browserbase).
Another set of companies is giving agents more utility across payments (which includes companies developing crypto wallets for agents as well as virtual cards) and voice (development platforms and tools for testing AI voice applications as well as speech models).
Meanwhile, demand for simplified, comprehensive deployment options is driving the rise of AI agent development platforms — the most crowded infrastructure market on our map.
LLM developers including Cohere (with its North AI workspace) and Mistral have launched their own agent development frameworks, while Amazon, Microsoft, Google, and Nvidia all offer AI agent development tooling. With many enterprises favoring established vendors due to lower risk, big tech companies have significant advantages here.
Trust & performance
Concerns around reliability and security have helped establish a market for agent evaluation & observability tools. Early-stage companies are targeting applications such as automated testing (e.g., Haize Labs) and performance tracking (e.g., Langfuse).
Multi-agent systems, where specialized sub-agents work together to complete tasks, also show promise in improving accuracy. Insight Partners-backed CrewAI’s multi-agent orchestration platform is reportedly already used by 40% of the Fortune 500.
Vendors are also tackling reliability concerns directly. Based on our briefings with 20+ AI agent startups in Q1’25, companies are using 5 primary methods to build user trust:
Transparency
Human oversight
Technical safeguards
Security & compliance
Continuous improvement
Horizontal applications & job functions
Horizontal AI agent startups make up nearly half of the map and overall landscape.
This segment primarily features startups targeting enterprises, with industry-agnostic applications across job functions like HR/recruiting, marketing, and security operations. Companies in the productivity & personal assistants market, including OpenAI with its Operator agent, are targeting consumers and employees directly.
The AI agent markets with the most traction — based on companies’ median Mosaic health scores — are customer service and software development (which includes coding and code review & testing agents). These markets are also among the most crowded due to the value agents bring to well-defined workflows and testable environments.
We see this reflected in adoption, particularly at the customer service layer: Among 64 organizations surveyed by CB Insights in December 2024, two-thirds indicated they are using or will be using AI agents in customer support in the next 12 months.
Overall, horizontal AI agent applications are more commercially mature compared to the infrastructure and vertical segments, with over two-thirds of the market deploying or scaling their solutions based on CBI Commercial Maturity scores.
Vertical (industry-specific) applications
We expect increasing verticalization as startups carve out niches by solving industry-specific customer problems, especially in areas with strict regulatory scrutiny and data sensitivity.
This category features companies catering to industries including:
Financial services & insurance: The most crowded vertical category on the map with 11 companies, startups here are targeting a variety of finserv workflows such as financial research (Boosted.ai and Wokelo), insurance sales & support (Alltius and Indemn), and wealth advisory prospecting & operations (Finny AI and Powder).
Healthcare: Solutions in this market aim to reduce the volume of manual tasks for healthcare professionals across use cases like clinical documentation, revenue cycle operations, call centers, and virtual triage. Solutions from companies like Thoughtful AI (revenue cycle operations) and Hippocratic AI (staffing marketplace) are targeting end-to-end healthcare workflows.
Industrials: These companies look to optimize processes and equipment — including control systems, robots, and other industrial machines — without relying on consistent human intervention. For example, Composabl launched an agent platform in May 2024 that uses LLMs to create skills and goals for agents that can control industrial equipment. Public companies like Palantir are also active in this space. Learn more in our industrial AI agents & copilots market map.
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