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Map the AI agent market
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Audit the onboarding funnel
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Map the AI agent market
Total: 2.1K
Map the AI agent market for Q4 and write a brief on how the top five players position themselves
Worked for 0s
I'll pull the Q4 funding rounds first, then read how each of the top five describes itself.
3 actions
Search web · AI agent startup funding Q4
Read page · crunchbase.com/discover/funding-rounds
Extract · 14 rounds · 5 vendor pages
Positioning has moved since Q3 — writing that up with the funding table alongside it.
2 actions
Eigent is working…

Here is the Q4 agent-market brief.

  • $2.1B raised across 14 rounds, concentrated in orchestration and computer-use startups.
  • Four of the five leaders now lead with “multi-agent workforce” rather than “copilot”.
  • Pricing is moving off seats toward task- and outcome-based tiers.
ai_agent_market_q4.mdfunding_rounds_q4.csv
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EigentHigh
Summary
Latest only
Agents1
CAMEL Agent
Progress3
Collect Q4 funding rounds
Compare positioning for the top five
Assemble the shareable brief
Execution Context3
Skills1
market-research
MCP & Tools2
Exa Search
Notion
Files

Files the agent writes during this session appear here.

ai_agent_market_q4.md
funding_rounds_q4.csv

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AWS
Booking.com
HSBC
Tencent
Baidu
Imperial College London
University of Oxford
ETH Zurich
University of Chicago
Chinese University of Hong Kong
KAUST
Eigent
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Market ResearchCompetitive and market research for the Q4 planning cycle. Agents, Skills and Connectors are packaged here once and reused by every session.
Sessions12
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StatusActive
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AddedJun 2, 2026
SessionsTasksAutomationsContextMemorySpace Settings
Open Workspace
NameSpaceTasksAutomationsUpdated
Map the AI agent marketMarket Research612m ago
Weekly competitor digestMarket Research411h ago
Summarize Q3 customer interviewsMarket Research90Yesterday
Pricing teardown: top five vendorsMarket Research702d ago
Draft the Q4 positioning memoMarket Research514d ago
Audit the onboarding funnelMarket Research110Jun 18
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Build a CAD Model from Video: Gemini 3.7 Flash vs. 3.6 Flash

Build a CAD Model from Video: Gemini 3.7 Flash vs. 3.6 Flash

Analyze the transformers character in the uploaded reference video and generate a production-ready, highly detailed 3D CAD/mesh model exported as a .glb file. Analyze the character identity and style, joints and articulation, and surface details. Create ultra-high-precision hard-surface mechanical geometry with distinct body and armor pieces separated around logical joint pivot points. Use metallic PBR materials, including anodized metals, brushed steel, dark titanium, high-gloss painted armor, and emissive channels for the eyes, chest core, and joint glow effects. Pack the diffuse, normal, roughness, metallic, and emissive textures directly into the GLB. Ensure clean topology, manifold geometry, and real-time optimization without losing mechanical sharpness.

Automated VAT Return from Receipts and Invoices

Automated VAT Return from Receipts and Invoices

Please process all receipts and invoices in the "VAT" folder, including photos, scanned PDFs, and digital invoices. The final output should include only two files: (1) vat_return.xlsx — the Excel file should include one row per receipt or invoice, list all extracted fields, show whether each item is eligible for VAT recovery, show the recoverable VAT amount for each eligible item, include the exclusion reason for non-recoverable items, clearly flag items that require manual review, and include a summary sheet showing the total recoverable VAT amount. (2) vat_return.html — create a self-contained HTML file that can be opened directly and shared with the accounting team. The HTML file should show all VAT recovery items, the recoverable VAT amount for each item, excluded items and the reasons for exclusion, items requiring manual review, and the total recoverable VAT amount. Do not guess any uncertain information.

Long-Horizon Task: GLM-5.1 vs GLM-5.2 on Eigent

Long-Horizon Task: GLM-5.1 vs GLM-5.2 on Eigent

Do a deep-dive research on 26 companies in the AI infrastructure ecosystem — the most certain main thread of the entire AI value chain. Cover these 6 sub-sectors (pick representative companies in each, from large-cap leaders down to smaller players): AI Data Center (compute infrastructure / build-out); GPU / AI Chips (training & inference silicon, ASICs, IP); Servers, Networking & Optical Modules (switches, NICs, optical interconnect); Power, Liquid Cooling & Energy Storage (power supply, thermal, energy management); AI Cloud / Compute Platform (hyperscalers, GPU clouds, compute-rental platforms); Supporting Ecosystem (HBM / advanced packaging, foundry, connectors & other critical components). For each company, research: company name, sub-sector, HQ / country; core products and its specific role in the AI chain; public or private (ticker + exchange if listed; if private, note latest valuation / funding round); market cap or valuation size (used for ranking); positioning and moat in the ecosystem (1–2 sentences); key customers / competitors. Ordering: within each sub-sector, rank from largest to smallest (by market cap / valuation). Structure the whole thing top-down: from the full hardware-ecosystem landscape → down to each individual company. Output requirements: First, generate a structured data file ai_infra_data.json — containing all 26 companies with the fields above, the 6 sub-sector classifications, a public/private flag, and a cross-company comparison matrix (sub-sector × key dimensions). Then generate a polished HTML report from that JSON: include an ecosystem landscape / layered diagram, sector sections, company cards, a clear visual indicator for public vs. private (tags or color coding), a market-cap ranking chart, and a sortable/filterable comparison table. Make the design professional, information-dense, and interactive. Verify the research data for accuracy first (listing status, tickers, valuations — use the latest figures and cite sources), then generate the report. Send the task in single-agent mode.

Build 10 Chinese New Year HTML5 Games with Eigent

Build 10 Chinese New Year HTML5 Games with Eigent

Build 10 separate and COMPLETE games with topics related to 2026 Chinese New Year (Horse) in HTML, CSS and JS (no libraries). Games must be fun, original, polished, mobile-friendly. Include scoring, scaling difficulty, restart buttons, and smooth visuals. Cover: arcade, puzzle, endless runner, reaction, strategy, memory, 2-player local, idle, retro pixel, and 1 experimental game.

Build a 3D Snow Bros Platformer with Gemini 3.1 Pro

Build a 3D Snow Bros Platformer with Gemini 3.1 Pro

Create a modern 3D side-scrolling platformer inspired by Mario, combined with Snow Bros mechanics. The player can shoot snow projectiles to freeze monsters into snowballs, then kick them to chain into other enemies. Include a scoring system, lives display, scaling difficulty, and a restart function with rich 3D layered environments.

Configure Gemini & Automate Salesforce Deals

Configure Gemini & Automate Salesforce Deals

I need to update the salesforce.com - 200 Widgets deal. Give me the contact name and phone number. Back to the Opportunities page, edit the Next Step as 'book a meeting with + the contact name and phone number.'

Organize Your Desktop & Get an HTML Daily Report

Organize Your Desktop & Get an HTML Daily Report

Off work now! Please help me organize the work files on my desktop into today's folder, and then write an HTML daily report summarizing what I did today.

Add a New Salesforce Contact and Account with MiniMax M2

Add a New Salesforce Contact and Account with MiniMax M2

We have a new contact at Global Media - Jennifer Martinez (jennifer.m@globalmedia.com) is their new Senior Marketing Manager. Add her to our Salesforce and make sure she's connected to the right company. You can open the browser directly—I'm already logged into the system.

Turn Excel Sales Data into a Report with Kimi K2.5

Turn Excel Sales Data into a Report with Kimi K2.5

We are conducting a sales performance evaluation. Analyze the sales data and compare all salespeople across key metrics including order count, total revenue, average/median/highest order value, GMV per person per month, and order conversion rate. Generate a structured HTML analytics report with a summary, comparison table, and conclusions.

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