Wide Research: Manus Unleashes 100-Agent Parallel Processing for Lightning-Fast, Large-Scale Insight

 Manus—the Singapore-based startup behind the namesake autonomous AI agent—has flipped the research workflow on its head with Wide Research, a system-level mechanism that sends hundreds of parallel agents after every angle of a complex question. Whether you want a side-by-side on 500 MBA programs or a 360° scan of GenAI tools, Wide Research chews through the workload in a fraction of the time sequential agents would take. 

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From Deep to Wide

Most “deep research” agents operate like meticulous librarians: a single high-capacity model crawls source after source, sequentially synthesising answers. It’s thorough—but agonisingly slow at scale. Wide Research replaces that linear approach with an agent-cluster collaboration protocol. Each sub-agent is a full Manus instance, not a narrow specialist, so any of them can read, reason and write. The orchestration layer splinters a task into sub-queries, distributes them, then merges the results into one coherent report. 

Why general-purpose sub-agents matter

Traditional multi-agent designs hard-code roles—“planner,” “coder,” “critic.” Those rigid templates break when a project veers off script. Because every Wide Research worker is general-purpose, task boundaries dissolve: one sub-agent might scrape SEC filings, another might summarise IEEE papers, and a third could draft executive bullets—then hand the baton seamlessly. 

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Inside the Architecture

Layer	Function	Default Tech
Task Decomposer	Splits the master query into 100-plus granular prompts	LLM-based planner
Agent Fabric	Launches isolated, cloud-hosted Manus instances; scales elastically	K8s + Firecracker VMs
Coordination Protocol	Routes intermediate results, resolves duplicates, merges insights	Proprietary RPC
Aggregator & Formatter	Synthesises final doc, slides, or CSV	Manus core model

The entire pipeline is asynchronous; users can park a query (“compare 1 000 stocks”) and return later to a ready-made dashboard—no tab babysitting required. 

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Performance Snapshot

Scenario	Deep-style Single Agent	Wide Research (100+ agents)
Analyse 100 sneakers for price, reviews, specs	~70 min	< 7 min
Rank Fortune 500 by AI spend, ESG score	~3 h	18 min
Cross-compare 1 000 GenAI startups	Time-out	45 min

(Internal Manus demo data shown during launch.) 

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Early Use Cases

1. Competitive Intelligence – Product teams ingest hundreds of rival SKUs, markets and patents overnight.

2. Financial Screening – Analysts filter thousands of equities or tokens with bespoke metrics—faster than spreadsheet macros can update.

3. Academic Surveys – Researchers pull citations across disciplines, summarising 200+ papers into thematic clusters in a single afternoon.

Because Wide Research is model-agnostic, enterprises can plug in Anthropic Claude, Qwen, or local Llama checkpoints to meet data-sovereignty rules. 

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Pricing & Roll-Out

• Today: Wide Research is live for Pro subscribers (US $199/month).

• Q3 2025: Gradual access for Plus and Basic tiers.

• Future: Manus hints at an on-prem “WideKit” for regulated industries that can’t leave their firewall. 

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Limitations & Trade-Offs

• Compute Cost: Hundreds of VM-backed agents aren’t cheap; budget accordingly for very large jobs.

• Cold-Start Results: Until sub-agents gather enough signal, early outputs can be uneven—iteration helps.

• Benchmark Transparency: Manus hasn’t yet published formal speed/quality benchmarks vs. sequential baselines, though third-party analyses are emerging. 

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The Bigger Picture

Wide Research is less a one-off feature than a proof-of-concept for “scaling laws of agentic AI.” Manus argues that throwing more capable agents—not merely larger context windows—can yield super-linear gains in throughput and idea diversity. It’s a thesis with broad implications for everything from autonomous coding swarms to AI-driven drug pipelines.

As parallel agent frameworks proliferate (think IBM’s MCP Gateway, Baidu’s AI Search Paradigm, Anthropic’s Claude tool plugins), context engineering and agent coordination will rival model size as the key levers of performance.

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Key Takeaway

Wide Research reframes high-volume, messy analysis as a parallel rather than serial challenge—turning hours of manual slog into minutes of delegated computation. For teams drowning in data and deadlines, Manus just opened a wormhole to faster, broader insight—no prompt cajoling required.

From Tedious Edits to Autonomous IDEs: How Kiro’s AI Agent Hooks Turbo-Charge Your Dev Workflow

 The modern codebase is a living organism: files mutate, requirements shift, tests trail behind and docs go stale. Kiro—the Amazon-backed, Claude-powered IDE—thinks the fix is automation that lives inside your editor. On July 16 2025 the team introduced Agent Hooks, a rules-engine plus AI copilot that fires the moment you hit “save” or merge a pull request.

What exactly is an Agent Hook?

Each hook couples a trigger (file edit, creation, deletion, or even a manual slash-command) with an AI action such as “update the related unit tests” or “refresh my README” . Unlike brittle shell scripts, the action is described in plain English and executed by a Gemini-class agent that understands project context. The result feels less like CI glue and more like a junior dev who never sleeps.

Five headline benefits

1. Natural-language config – type “Whenever I touch *.py, update the matching test_*.py” and the hook YAML writes itself.

2. Context-aware reasoning – the agent sees your entire workspace, so it can refactor imports or respect custom test frameworks.

3. Real-time execution – actions run instantly, keeping flow intact instead of kicking chores to a nightly job.

4. Shareable recipes – hook files live in .kiro/hooks, so teams version them like code and inherit automation on git pull.

5. Stack-agnostic events – docs list triggers for save, create, delete, plus a user-initiated option for ad-hoc tasks.

Building your first hook in three clicks

Open Kiro’s sidebar, hit “Agent Hooks ➕”, and either select a template or just describe what you need. The UI scaffolds a config you can fine-tune—patterns, prompt, and whether it auto-runs or waits for manual confirmation. Behind the scenes, Kiro writes a .kiro.hook file so you’re always one git diff away from auditing the logic.

Real-world recipes

• Test synchroniser – Every Python edit triggers the agent to inspect changes and regenerate the paired test module, ensuring 100 % coverage drifts aren’t ignored.

• Doc updater – Modify a public API and the hook patches your Markdown docs so onboarding guides never lag behind shipping code.

• Git concierge – On commit, a hook can draft a concise changelog entry and polish the commit message to match team conventions.

• I18N helper – Save a UI string file and watch the agent push auto-translations to language packs.

Best-practice tips

Start small—a single file pattern and a succinct prompt—then iterate by reading the hook execution history shown in Kiro’s chat pane. Give the agent richer guidance (“follow Google Python Style”) and reference project docs inside the prompt for tighter alignment. Finally, commit hooks so teammates inherit them; over time your repo becomes a cookbook of living automation rules the whole squad benefits from.

Why this matters

Developers already rely on AI for autocomplete and chat, but those tools are reactive—you ask, they answer. Agent Hooks flip the script to proactive assistance that runs without explicit prompts, erasing the cognitive tax of context switching. In a world of sprawling microservices and relentless release cadences, the ability to delegate routine upkeep to an always-on agent is a genuine force multiplier.

Kiro doesn’t claim to replace developers; it aims to amplify craftsmanship by letting humans stay in the creative loop while machines patrol the trenches. If your backlog is clogged with “fix tests” and “update docs” tickets, Agent Hooks might be the invisible intern you’ve been wishing for. Install Kiro, write your first hook, and watch housekeeping melt away—one automated trigger at a time.

“Everyone’s AI”: MiniMax CEO Junjie Yan Reimagines the AI Economy at WAIC 2025

 The opening morning of the World Artificial Intelligence Conference 2025 (WAIC) in Shanghai was buzzing with hardware demos and multimodal avatars, yet the moment that set the tone for the three-day summit was a keynote titled “Everyone’s AI.” Delivered by MiniMax founder & CEO Junjie Yan, the talk argued that artificial intelligence is no longer a sidecar to the internet economy—it is becoming the primary productive force. 

From research toy to societal engine

Yan traced a 15-year personal journey in AI research, noting that tasks once handled by junior engineers—code writing, data annotation, even literature review—are now 70 % automated inside MiniMax. The implication is stark: as models grow more capable, human attention shifts from mechanical chores to creative orchestration. “AI can now write the software that analyzes the data we used to comb through by hand,” he observed, positioning large models as multipliers of both knowledge work and imagination. 

The economics: another 10× drop on the horizon

MiniMax isn’t just waxing philosophical; it is betting on cost curves. Yan predicted that inference prices for top-tier models will fall another order of magnitude within two years, echoing the steep declines seen in 2024–25. Cheaper inference, he argued, is the real catalyst for mass adoption—unlocking agentic workflows that might consume millions of tokens per session without breaking budgets. 

Many models, many values

Contrary to fears of an AI monoculture, Yan expects plurality to define the market. Alignment targets diverge—one model may optimize for programming accuracy, another for empathetic conversation—so “there will definitely be multiple players,” he insisted. Open-source ecosystems, now approaching closed-source performance, reinforce that trend. 

Multi-agent systems change the rules

Inside MiniMax’s own products—Conch AI for voice, M-Series for reasoning, and the new MiniMax-M1 hybrid model—multi-agent architectures are displacing single-model pipelines. In such systems, the marginal advantage of any one model shrinks, while orchestration and tool-use matter more. That, Yan believes, will democratize expertise: startups armed with well-designed agent swarms can challenge giants who merely scale parameters. 

A less money-burning industry

Dropping costs and smarter experiment design mean AI R&D need not be an endless bonfire of GPUs. MiniMax’s internal stats show 90 % of routine data analysis already handled by AI, freeing researchers to pursue “genius ideas” that compound returns faster than raw compute. If training becomes less capital-intensive and inference goes bargain-basement, the barriers to entry for niche models and vertical agents collapse. 

“Everyone’s AI” as call to action

Yan closed by reframing access as both economic necessity and moral imperative: AGI, when achieved, should belong to multiple companies and a broad user base—not a solitary gatekeeper. He tied the mission to a Chinese proverb about unleashing creativity: lower thresholds ignite countless sparks. For a conference that also featured Geoffrey Hinton warning about rogue super-intelligence, MiniMax’s pitch provided a complementary optimism grounded in unit economics and open ecosystems.

Why it matters

The keynote crystallizes a broader shift in 2025: value is migrating from parameter counts to deployment fluency, from cloud monopolies to community forks, and from eye-watering API bills to near-frictionless inference. If Yan’s forecast holds, the next two years could see AI agents embedded in every workflow—powered by models cheap enough to run continuously and diverse enough to reflect local values. In that future, “Everyone’s AI” is not a slogan; it is table stakes.