Writer, the enterprise AI company with a valuation of $1.9 billion, unveiled an autonomous “super agent” on Tuesday. This agent can autonomously carry out complex, multi-step business tasks across numerous software platforms, marking a substantial advancement in the corporate AI competition.

The introduction of the Action Agent signifies a major evolution from AI chatbots that merely provide answers to questions, to systems that can autonomously complete entire projects. This agent can navigate websites, analyze data, create presentations, write code, and manage tasks across an organization’s entire tech infrastructure without needing human intervention.

“Other AI chatbots can tell you what to do,” said May Habib, Writer’s CEO and co-founder. “Action Agent does it. It’s like the difference between receiving a research report and having your entire sales pipeline updated and executed upon.”

This launch positions San Francisco-based Writer as a strong contender against Microsoft’s Copilot and OpenAI’s ChatGPT in the profitable enterprise market. Unlike consumer-targeted AI tools, Writer’s agent includes enterprise-grade security controls and audit trails that are necessary for regulated industries such as banking and healthcare.

How Writer’s super agent executes tasks other AI can only describe

Writer’s Action Agent stands apart from existing AI assistants by operating at what the company refers to as “level four orchestration” — the pinnacle of AI automation. Most contemporary enterprise AI tools function at levels one or two, performing basic tasks like answering questions or retrieving documents.

“What we’ve done here is full orchestration,” Matan-Paul Shetrit, Writer’s head of product, explained in an interview with VentureBeat. “This is an agent that calls other agents, creates its own tools when necessary, and can execute with complete visibility.”

The distinction goes well beyond mere automation. While traditional AI assistants like ChatGPT or Copilot are primarily designed for a Q&A experience, Shetrit noted that Action Agent is built for execution. “The difference is not, let me do this back and forth brainstorming, but more, once I want to do the brainstorming, I can act on it.”

The agent operates within its own isolated virtual environment for each session, enabling it to independently browse web pages, develop software, resolve technical issues, and execute complex multi-step plans. When tasked with performing a product analysis, for example, Action Agent will automatically process thousands of customer reviews, conduct sentiment analysis, identify themes, and generate a presentation — all without human guidance.

The system’s capabilities extend to creating its own tools when existing ones are insufficient. “It can take action whether or not it has MCP or any tool access, because it can just generate its own tools on the fly for the task,” Shetrit explained.

During a demonstration, Shetrit showcased the agent conducting clinical trial site selection — a process that typically requires weeks of human research. The agent systematically analyzed demographics across multiple cities, ranked locations by suitability criteria, and generated comprehensive reports with supporting evidence.

“This is weeks worth of work by these companies,” Shetrit noted. “It’s not something that’s trivial to do.”

Breaking benchmarks: Action agent outperforms OpenAI on key tests

Writer’s claims about capabilities are supported by impressive benchmark results. Action Agent scored 61% on GAIA Level 3, the most challenging benchmark for AI agent performance, outperforming competing systems including OpenAI’s Deep Research. The agent also achieved a 10.4% score on the CUB (Computer Use Benchmark) leaderboard, making it the top performer for computer and browser use tasks.

These results demonstrate the agent’s proficiency in handling complex reasoning tasks that have traditionally been challenging for AI systems. GAIA Level 3 tests require agents to navigate multiple tools, synthesize information from various sources, and complete multi-step workflows — precisely the kind of work that enterprises need automated.

The performance is driven by Writer’s Palmyra X5 model, which boasts a one-million-token context window — enough to process hundreds of pages of documents simultaneously while maintaining coherence across complex tasks. This extensive context capability allows the agent to work with entire codebases, lengthy research reports, and comprehensive datasets without losing sight of the overall goal.

Why enterprise security sets Writer apart from consumer AI tools

Writer’s focus on enterprise needs distinguishes it in a market dominated by consumer-oriented AI companies attempting to adapt their products for business use. The company built Action Agent on its existing enterprise platform, which already serves hundreds of major corporations, including Accenture, Vanguard, Qualcomm, Uber, and Salesforce.

This distinction is crucial for enterprise adoption. While consumer AI tools often operate as “black boxes” with limited transparency, Writer’s system provides complete audit trails showing exactly how the agent reached its conclusions and what actions it took.

Shetrit emphasized this transparency as essential for regulated industries: “If you start talking about some of the largest companies in the world, whether it’s banks, pharmaceutical, or healthcare, it’s unacceptable that you don’t know how these autonomous agents are behaving and what they’re doing.”

The system provides “full traceability, auditability, and visibility,” allowing IT administrators to set detailed permissions controlling which tools each agent can access and what actions they can perform.

Connecting 600+ business tools without breaking enterprise security

Action Agent’s ability to connect with more than 600 enterprise tools is a significant technical achievement. The agent uses Model Context Protocol (MCP), an emerging standard for AI tool integration, but Writer has enhanced it with enterprise-grade controls that address security and governance concerns.

Writer has been collaborating closely with Amazon Web Services and other industry players to bring MCP to enterprise standards. “There’s still a place to bring it to enterprise grade,” Shetrit noted, referencing recent issues with MCP implementations at companies like Asana and GitHub.

The company’s approach allows granular control that extends beyond simple user permissions. “It’s not just by a user,” Shetrit explained. “It will also have it by the specific agent. So as an IT persona or a security persona, I have the controls I need to feel comfortable with this data access.”

For instance, administrators can allow certain agents to publish messages to Slack while preventing them from deleting messages. “You need that fine-grained control, and that’s something we’re integrating as part of the system,” Shetrit said.

The company pre-announced support for over 600 different tools, each offering detailed control both at the integration level and for specific agents. This capability allows Action Agent to coordinate work across an organization’s entire technology ecosystem, from customer relationship management systems to financial databases.

Free AI agents challenge traditional software pricing models

Writer’s decision to offer Action Agent free to existing customers challenges traditional software pricing models and reflects broader shifts in the AI industry. The move comes despite the significant computational costs associated with the agent’s extensive token usage.

“Token pricing is extremely problematic when you start thinking about enterprises,” Shetrit explained. “They need a budget line item. They need to figure out the cost structure. This highly variable cost model does not work for these companies, and that is why we’ve been moving away from this for a while now.”

The strategy reflects Writer’s confidence in its cost-efficient model development. The company spent just $700,000 to train its Palmyra X4 model, compared to an estimated $4.6 million for a similarly sized OpenAI model. This efficiency stems from Writer’s use of synthetic data and innovative training techniques that reduce computational requirements.

Writer’s reasoning for the free offering goes beyond competitive positioning. “We think this shows the full value of the ecosystem and the platform, and really starts delivering on the promise of AI,” Shetrit said. Internal users have reported being more excited about this AI product than any previous AI tool they’ve used, including other copilot systems.

Enterprise AI market heats up as startups target Microsoft and Google

Writer’s Action Agent launch escalates competition in the rapidly expanding enterprise AI market, which is projected to grow from $58 billion to $114 billion by 2027. The company competes directly with Microsoft’s Copilot suite, Google’s enterprise AI offerings, and OpenAI’s business products, but targets a different market segment with its enterprise-first approach.

The competitive positioning reflects a broader industry split between companies building general-purpose AI systems and those focusing specifically on enterprise needs. Writer’s approach prioritizes security, governance, and reliability over raw capability, betting that enterprise customers will choose specialized tools over consumer products adapted for business use.

Shetrit emphasized: “We are fully on the enterprise B-to-B side.”

This focus has paid off financially. Writer raised $200 million in Series C funding in November 2024 at a $1.9 billion valuation, nearly quadrupling its previous valuation. The round was co-led by Premji Invest, Radical Ventures, and ICONIQ Growth, with participation from major enterprise players including Salesforce Ventures, Adobe Ventures, and IBM Ventures.

From automation to transformation: How AI will reshape corporate work

Writer’s vision extends beyond automation to the fundamental reshaping of enterprise operations. The company identifies two clusters of emerging use cases: traditional “90% workflow, 10% AI” optimization and new “90% AI, 10% workflow” experiences that unlock entirely new capabilities.

“Each employee will have a thing like this next to them that helps them do their work, automate a lot of it, so they can do much higher leverage work across the organization,” Shetrit predicted.

This transformation addresses a critical shift in enterprise software expectations. As employees become accustomed to sophisticated AI tools in their personal lives, enterprise software must match or exceed that quality. “You cannot afford for enterprise software to not be as good, and in a lot of cases, significantly better,” Shetrit noted.

The shift is already changing internal dynamics at Writer itself. “Historically, execution was the bottleneck,” Shetrit explained. So as a PM he could always say no because he didn’t have capacity.”

But “capacity is no longer the bottleneck.” When his product managers claim they don’t have time for projects, he now uses Action Agent to generate “at least 70% of the work for them.”

This represents a fundamental change from “scarcity to an abundance mentality” that will require “a lot of retraining element that has to happen within the org.”

Inside Writer’s collaboration with Uber to build real-world AI agents

Writer’s collaboration with Uber on Action Agent illustrates how customer relationships improve its technology. Uber’s AI Solutions team provided operational expertise for scaling high-quality annotations across complex enterprise domains, while simultaneously validating the agent’s capabilities in real-world use cases.

“Our collaboration allowed us to contribute our deep operational expertise in high-quality data annotation to help shape an agent capable of tackling the most complex enterprise challenges,” said Megha Yethadka, GM and head of Uber AI solutions.

This partnership model allows Writer to develop agents that solve actual enterprise problems rather than theoretical use cases. The approach has generated diverse applications across industries, from HR candidate sourcing and securities analysis to clinical trial site selection and competitive intelligence.

Shetrit noted that customer creativity continues to surprise the team. Just a week from now, “I’ll have completely different use cases, because our customers will be very, very creative.”

What’s next: Rollout timeline and enterprise adoption strategy

Writer plans to expand Action Agent’s capabilities significantly over the coming weeks. The company will add connections to 80 enterprise platforms and third-party data providers like PitchBook and FactSet, enabling access to the full suite of 600-plus agent tools.

The rollout strategy reflects lessons learned from enterprise AI deployments. Rather than launching with full capabilities, Writer is starting with core functionality and gradually adding integrations based on customer feedback and real-world testing.

Action Agent is available immediately in beta to Writer’s existing customer base, with a 14-day trial available for new users. The gradual rollout allows the company to refine the system based on enterprise feedback while maintaining the security and reliability standards that regulated industries require.

The launch signals a pivotal moment in the enterprise AI revolution, where autonomous agents are moving from experimental curiosities to mission-critical business tools. As traditional software vendors scramble to add AI features to existing products, Writer’s agent-first approach may determine which companies successfully navigate the transition from human-driven to AI-augmented work.

But perhaps the most telling sign of this shift came from Shetrit himself: “We will all become, ‘managers’ of these fleets of agents, whether they’re humans or synthetic.”

In this future, the companies that learn to orchestrate AI agents alongside human workers may find themselves with an insurmountable advantage over those still clinging to purely human-driven processes.