Chinese language AI startup Manus, which made headlines earlier this yr for its strategy to a multi-agent orchestration platform for customers and “pro”-sumers (professionals desirous to run work operations), is again with an attention-grabbing new use of its know-how.
Whereas many different main rival AI suppliers corresponding to OpenAI, Google, and xAI which have launched “Deep Research” or “Deep Researcher” AI brokers that conduct minutes or hours of intensive, in-depth internet analysis and write well-cited, thorough reviews on behalf of customers, Manus is taking a distinct strategy.
The corporate simply introduced “Wide Research,” a brand new experimental characteristic that permits customers to execute large-scale, high-volume duties by leveraging the ability of parallelized AI brokers — much more than 100 at a single time, all targeted on finishing a single activity (or collection of sub-tasks laddering up mentioned overarching objective).
Manus was beforehand reported to be utilizing Anthropic Claude and Alibaba Qwen fashions to energy its platform.
The AI Influence Sequence Returns to San Francisco – August 5
The subsequent part of AI is right here – are you prepared? Be a part of leaders from Block, GSK, and SAP for an unique take a look at how autonomous brokers are reshaping enterprise workflows – from real-time decision-making to end-to-end automation.
Safe your spot now – house is restricted: https://bit.ly/3GuuPLF
Parallel processing for analysis, summarization and inventive output
In a video posted on the official X account, Manus co-founder and Chief Scientist Yichao ‘Peak’ Ji reveals a demo of utilizing Huge Analysis to match 100 sneakers.
To finish the duty, Manus Huge Analysis almost immediately spins up 100 concurrent subagents — every assigned to investigate one shoe’s design, pricing, and availability.
The result’s a sortable matrix delivered in each spreadsheet and webpage codecs inside minutes.
The corporate suggests Huge Analysis isn’t restricted to information evaluation. It may also be used for inventive duties like design exploration.
In a single state of affairs, Manus brokers concurrently generated poster designs throughout 50 distinct visible types, returning polished property in a downloadable ZIP file.
In keeping with Manus, this flexibility stems from the system-level strategy to parallel processing and agent-to-agent communication.
Within the video, Peak explains that Huge Analysis is the primary utility of an optimized virtualization and agent structure able to scaling compute energy 100 occasions past preliminary choices.
The characteristic is designed to activate mechanically throughout duties that require wide-scale evaluation, with no guide toggles or configurations required.
Availability and pricing
Huge Analysis is obtainable beginning immediately for customers on Manus Professional plan and can steadily turn into accessible to these on the Plus and Primary plans. As of now, subscription pricing for Manus is structured as follows monthly.
Free – $0/month Contains 300 every day refresh credit, entry to Chat mode, 1 concurrent activity, and 1 scheduled activity.
Primary – $19/month Provides 1,900 month-to-month credit (+1,900 bonus throughout restricted provide), 2 concurrent and a couple of scheduled duties, entry to superior fashions in Agent mode, picture/video/slides era, and unique information sources.
Plus – $39/month Will increase to three concurrent and three scheduled duties, 3,900 month-to-month credit (+3,900 bonus), and consists of all Primary options.
Professional – $199/month Provides 10 concurrent and 10 scheduled duties, 19,900 credit (+19,900 bonus), early entry to beta options, a Manus T-shirt, and the complete characteristic set together with superior agent instruments and content material era.
There’s additionally a 17% low cost on these costs for customers who want to pay up-front yearly.
The launch builds on the infrastructure launched with Manus earlier this yr, which the corporate describes as not simply an AI agent, however a private cloud computing platform.
Every Manus session runs on a devoted digital machine, giving customers entry to orchestrated cloud compute by pure language — a setup the corporate sees as key to enabling true general-purpose AI workflows.
With Huge Analysis, Manus customers can delegate analysis or inventive exploration throughout dozens and even a whole lot of subagents.
In contrast to conventional multi-agent programs with predefined roles (corresponding to supervisor, coder, or designer), every subagent inside Huge Analysis is a completely succesful, totally featured Manus occasion — not a specialised one for a selected function — working independently and in a position to tackle any normal activity.
This architectural choice, the corporate says, opens the door to versatile, scalable activity dealing with unconstrained by inflexible templates.
What are the advantages of Huge over Deep Analysis?
The implication appears to be that working all these brokers in parallel is quicker and can lead to a greater and extra assorted set of labor merchandise past analysis reviews, versus the one “Deep Research” brokers different AI suppliers have proven or fielded.
However whereas Manus promotes Huge Analysis as a breakthrough in agent parallelism, the corporate doesn’t present direct proof that spawning dozens or a whole lot of subagents is more practical than having a single, high-capacity agent deal with duties sequentially.
The discharge doesn’t embrace efficiency benchmarks, comparisons, or technical explanations to justify the trade-offs of this strategy — corresponding to elevated useful resource utilization, coordination complexity, or potential inefficiencies. It additionally lacks particulars on how subagents collaborate, how outcomes are merged, or whether or not the system gives measurable benefits in velocity, accuracy, or price.
Consequently, whereas the characteristic showcases architectural ambition, its sensible advantages over less complicated strategies stay unproven primarily based on the data supplied.
Sub-agents have a combined monitor report extra usually, to this point…
Whereas Manus’s implementation of Huge Analysis is positioned as an development generally AI agent programs, the broader ecosystem has seen combined outcomes with comparable subagent approaches.
For instance, on Reddit, self-described customers of Claude’s Code have raised issues about its subagents being sluggish, consuming massive volumes of tokens, and providing restricted visibility into execution.
Widespread ache factors embrace lack of coordination protocols between brokers, difficulties in debugging, and erratic efficiency throughout high-load durations.
These challenges don’t essentially mirror on Manus’s implementation, however they spotlight the complexity of creating sturdy multi-agent frameworks.
Manus acknowledges that Huge Analysis continues to be experimental and will include some limitations as improvement continues.
Trying forward
With the rollout of Huge Analysis, Manus deepens its dedication to redefining how customers work together with AI brokers at scale.
As different platforms wrestle with the technical challenges of subagent coordination and reliability, Manus’s strategy could function a take a look at case for whether or not generalized agent cases — fairly than narrowly scoped modules — can ship on the imaginative and prescient of seamless, multi-threaded AI collaboration.
The corporate hints at broader ambitions, suggesting that the infrastructure behind Huge Analysis lays the groundwork for future choices. Customers and business watchers alike will likely be paying shut consideration as to if this new wave of agent structure can dwell as much as its potential — or whether or not the challenges seen elsewhere within the AI house will finally catch up.
Day by day insights on enterprise use instances with VB Day by day
If you wish to impress your boss, VB Day by day has you coated. We provide the inside scoop on what firms are doing with generative AI, from regulatory shifts to sensible deployments, so you’ll be able to share insights for max ROI.
An error occured.
