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Meta joins enterprise agent race with Muse Wonder, facing a trust gap
The company’s new releases point to an AI market that is becoming more personal, with agents and models completing tasks, but enterprises still aren’t ready for this new phase of AI.
Meta introduced models on Monday that target personal and professional agentic tasks, capitalizing on the momentum of its successful Muse personal agent, released last month.
Muse Wonder and Muse Wonder Full expand on the launch of Meta Muse. Powered by the Muse Spark model, Muse is an AI agent that does tasks for individuals, such as web browsing, planning trips, making purchases and organizing emails.
The new models expand Muse’s capabilities by applying specialized knowledge to circumstances users encounter at work in domains such as law, finance, and health. Meta said. The vendor declared that the models -- the first frontier models the Facebook, Instagram and WhatsApp parent company has released to compete with Anthropic, OpenAI and Google -- are the next step in bringing personal superintelligence to its customers.
A new age
While the models are geared mainly toward consumers, they are also part of a fast-developing trend of AI agents capable of operating in enterprise environments.
“This has been the focus for the last year or two that we are moving away from chatbots and going to this agentic world where being able to delegate some tasks that these systems can do,” said Chirag Shah, a professor at the University of Washington in Seattle.
With Meta Muse, OpenAI Dots, and Gemini Agent, AI is on the verge of its next big milestone, he added. Other popular new personal agents include Grok Bot from SpaceXSI, Instinct and Hermes.
OpenClaw, now part of OpenAI, pioneered the personal agent trend only 10 months ago, but it was difficult to use. These agents are not.
Meta and enterprises
With Muse Wonder and the whimsically named Muse Wonder Full, Meta incorporated what it calls Active Memory and Active Focus. Using Active Memory, the models can remember what matters most based on critical information they received before and carry that across conversations or tasks. Active Focus enables the model to try alternative methods of carrying out a task or a goal in case the first way is unsuccessful.
“They’re really trying to steer these models to be more effective at personal agentic workflows,” said Arun Chandrasekaran, an analyst at Gartner. “They’re enabling these agents to be more specialized at daily tasks.”
It’s a step in the right direction, but despite the staccato cadence of recent releases of personal agents for consumers and enterprises, enterprises are not fully sold on them, Shah said.
“They are very hesitant,” he said, adding that many are unsure of whether they can trust the agent to do the right thing, and whether it will do anything harmful or unexpected.
“Trust in the sense of what it would do with my data beyond just doing the task. ‘How do we know that Meta is not looking into my data in a way that jeopardizes my position or my company’s position?’” Shah continued. “The companies that I talk to, they’re not there yet to be able to put this in place now.”
Businesses could also remain cautious about models like these because they need to determine whether they can connect them to applications they already use.
“At the end of the day, it’s all about integration,” said Sid Nag, founder and analyst at Tekonyx. “Does this particular functionality integrate with existing applications and cybersecurity constructs?”
“These interfaces are going behind the scenes and connecting to all these different applications. All of that integration is going to be what enterprises are going to look at,” he added.
Meta’s pricing
For Meta, Muse Wonder and Muse Wonder Full enable the social media giant to establish a revenue model for its AI agents, Chandrasekaran said. Previously, it had focused on free open source AI models to build its sprawling digital advertising business and expand its commercial ecosystem.
“They can’t keep giving away the compute in models for free,” he said. “One of the ways they can enable a revenue model for the AI agents is to start charging the consumer, but for them to charge the consumers, they need to deliver more highly capable models and more personalized AI agents.”
For developers, the model costs $4 per million input tokens and $20 per million output tokens. Developers who want access to Muse Wonder Full must subscribe to Muse Code -- Meta’s first dedicated coding agent. Muse Code costs $5 per month for everyday use, $15 per month for high usage, $50 per month for power, and $200 per month for maximum.
Beyond the consumer market, Meta is targeting the many small businesses that use Instagram, Facebook and WhatsApp to run their operations, and it can now offer AI agents on these social media platforms for those businesses.
For bigger enterprises, Meta still needs to build an entire distribution channel by partnering with vendors such as global systems integrator GSI and Salesforce, Chandrasekaran said.
Esther Shittu is an Informa TechTarget news writer and podcast producer and host covering AI software and systems.
