“The year of AI agents” is how 2024 is likely to be remembered in tech circles. A subcategory of generative AI, agents are catching on for the promise they hold to enable truly autonomous workflows that can free up humans for higher-level tasks.
Earlier this week, Writer announced it had raised $200 million for its Series C funding round, led by Premji Invest, Radical Ventures and Iconiq Growth. The fundraise thrust the developer of a full-stack generative AI platform for the enterprise into unicorn territory, with a pre-money valuation of $1.9 billion.
Writer’s ability to help customers expedite the production of marketing content and customer agreements – including internal legal and compliance workflows that often slow down the approval process – changes the way companies bring new products to market and has attracted such Fortune 500 customers as Franklin Templeton, Prudential, Qualcomm and Salesforce.
At Insight Partners’ ScaleUp AI conference this week, co-founder and CEO May Habib said that Writer’s end-user adoption rate helped boost the company’s valuation for its Series C funding.
In our previous article about agentic AI, Chip Hazard, a general partner at Flybridge Capital Partners, told Venture Capital Journal that the reason so much investment is “flowing into the space is that it’s a chance to completely rethink the application layer and what’s possible for both B2B and consumer applications.”
About one-third of enterprise software applications are projected to include AI agents by 2028, up from less than 1 percent this year, according to Gartner. Although VC managers tell us it’s too early to gauge what portion of total AI investment is being directed toward agentic AI tools or how fast investment is expected to rise in the years ahead, some analysts see the market growing from $5.1 billion today to $47.1 billion by 2030, as Speech Technology Magazine reported.
A survey of 100 senior IT leaders by Forum Ventures found that 48 percent of enterprises have already adopted AI agents in their workflows, while another 33 percent say they are ready to and are actively exploring solutions. Pressure from top executives and boards to deeply understand AI’s impact on their business is a key factor speeding adoption, said Jonah Midanik, chief operating officer and GP at Forum.
Popular use cases for AI agents in the enterprise field include automating sales and customer support, compliance for financial disclosures and marketing materials, human resources and payroll workflows, and business intelligence processes.
Investors think the real power of autonomous agents will be unleashed by multi-agent systems that link specific-task agents across various departments in an enterprise to execute more complicated tasks working together.
Deconstructing a workflow into individual steps to be executed in sequence by multiple agents requires a control flow tool that delineates that sequence, says Praveen Akkiraju, managing director at Insight Partners.
Insight led the $18 million multi-part Series A round of CrewAI in September because the company provides that “critical layer in the infrastructure required to build agentic going forward,” Akkiraju told VCJ.
While single-agent systems are production-ready, Tim Guleri, managing partner at Sierra Ventures, sees multi-agent systems as works in process within certain domains of the enterprise.
Although the promise of AI agents is vast, several challenges need to be addressed that could hinder broader adoption, including worries about reliability and security, uncertain regulatory frameworks and complexities around implementation.
Narrowing the trust gap with regard to AI agents will require being able to ensure deterministic outcomes from systems that are based on statistical probabilities, not inherent truths, say VC managers.
Because AI agents are systems rather than single pieces of code, building an agent is really a systems engineering problem, Akkiraju at Insight noted. “To get to be enterprise-class means you have the appropriate security and appropriate safeguards. Things like role-based access control.
“The real test for us in this generation of exciting AI developments is: what are the architectures and products that can truly scale to an enterprise level of deployment? We’re still in early days of that,” he said.
“The year of AI agents” is how 2024 is likely to be remembered in tech circles. A subcategory of generative AI, agents are catching on for the promise they hold to enable truly autonomous workflows that can free up humans for higher-level tasks.
Earlier this week, Writer announced it had raised $200 million for its Series C funding round, led by Premji Invest, Radical Ventures and Iconiq Growth. The fundraise thrust the developer of a full-stack generative AI platform for the enterprise into unicorn territory, with a pre-money valuation of $1.9 billion.
Writer’s ability to help customers expedite the production of marketing content and customer agreements – including internal legal and compliance workflows that often slow down the approval process – changes the way companies bring new products to market and has attracted such Fortune 500 customers as Franklin Templeton, Prudential, Qualcomm and Salesforce.
At Insight Partners’ ScaleUp AI conference this week, co-founder and CEO May Habib said that Writer’s end-user adoption rate helped boost the company’s valuation for its Series C funding.
In our previous article about agentic AI, Chip Hazard, a general partner at Flybridge Capital Partners, told Venture Capital Journal that the reason so much investment is “flowing into the space is that it’s a chance to completely rethink the application layer and what’s possible for both B2B and consumer applications.”
About one-third of enterprise software applications are projected to include AI agents by 2028, up from less than 1 percent this year, according to Gartner. Although VC managers tell us it’s too early to gauge what portion of total AI investment is being directed toward agentic AI tools or how fast investment is expected to rise in the years ahead, some analysts see the market growing from $5.1 billion today to $47.1 billion by 2030, as Speech Technology Magazine reported.
A survey of 100 senior IT leaders by Forum Ventures found that 48 percent of enterprises have already adopted AI agents in their workflows, while another 33 percent say they are ready to and are actively exploring solutions. Pressure from top executives and boards to deeply understand AI’s impact on their business is a key factor speeding adoption, said Jonah Midanik, chief operating officer and GP at Forum.
Popular use cases for AI agents in the enterprise field include automating sales and customer support, compliance for financial disclosures and marketing materials, human resources and payroll workflows, and business intelligence processes.
Investors think the real power of autonomous agents will be unleashed by multi-agent systems that link specific-task agents across various departments in an enterprise to execute more complicated tasks working together.
Deconstructing a workflow into individual steps to be executed in sequence by multiple agents requires a control flow tool that delineates that sequence, says Praveen Akkiraju, managing director at Insight Partners.
Insight led the $18 million multi-part Series A round of CrewAI in September because the company provides that “critical layer in the infrastructure required to build agentic going forward,” Akkiraju told VCJ.
While single-agent systems are production-ready, Tim Guleri, managing partner at Sierra Ventures, sees multi-agent systems as works in process within certain domains of the enterprise.
Although the promise of AI agents is vast, several challenges need to be addressed that could hinder broader adoption, including worries about reliability and security, uncertain regulatory frameworks and complexities around implementation.
Narrowing the trust gap with regard to AI agents will require being able to ensure deterministic outcomes from systems that are based on statistical probabilities, not inherent truths, say VC managers.
Because AI agents are systems rather than single pieces of code, building an agent is really a systems engineering problem, Akkiraju at Insight noted. “To get to be enterprise-class means you have the appropriate security and appropriate safeguards. Things like role-based access control.
“The real test for us in this generation of exciting AI developments is: what are the architectures and products that can truly scale to an enterprise level of deployment? We’re still in early days of that,” he said.
