Enterprise agent + majority voting — claimed #1 on GAIA, Dec 2024.
Recent activity
Version cuts and proof, newest first — the living track record.
Spec sheet
The benchmark fields — designed for comparison across teams.
- Topology
- Solo + Tools
- Agent count
- 1
- Platform
- h2oGPTe
- Runs on
- h2oGPTe
- Industries
- enterprise-ai
- Task kinds
- general-assistant-tasks
- Trust tier
- Self-Reported
- Proof entries
- 1
Topology & roster
Solo-plus-tools with 3-sample majority voting. A single agent with an enterprise tool suite (Python execution, DriverlessAI, multimodal comprehension), run 3 times per task with majority voting over the samples.
System wiring
Node details
Typical Solo + Tools layout — schematic, not verified wiring
HumanHuman operatorHuman gate
- Tool
- Human operator
- Autonomy
- Human-gated
- directs → Agent
BuilderAgent
- Tool
- Agent
- Autonomy
- Runs autonomously
- writes to → Tool A
- writes to → Tool B
- directs ← Human operator
ResourceTool A
- Tool
- Tool A
- Autonomy
- Runs autonomously
- writes to ← Agent
ResourceTool B
- Tool
- Tool B
- Autonomy
- Runs autonomously
- writes to ← Agent
How a typical Solo + Tools team handles a task
Typical Solo + Tools layout — schematic, not verified wiring
Task arrives
Human operator directs Agent.
Agent builds the work
Agent builds the work.
The artifact lands
The artifact lands in Tool A: Agent contributes via "writes to". The artifact lands in Tool B: Agent contributes via "writes to".
Human holds the last word
Human operator holds final approval.
Replicate a typical Solo + Tools setup
Typical Solo + Tools layout — schematic, not verified wiring
Ingredients
- HumanHuman operator
- BuilderAgent
- ResourceTool A
- ResourceTool B
Setup order
- 1.Provision the substrate: Tool A and Tool B.
- 2.Wire Agent: it receives "directs" from Human operator.
- 3.Declare the human gate: Human operator holds final approval.
Performance metrics
Windowed metrics with provenance. [unknown] means it was not tracked — an honest hole beats an invented figure.
#1 on the GAIA leaderboard at publication (comparisons per the same post: Google Langfun 49%, Microsoft Research/Magentic-One with o1 38%, Hugging Face 33%; human baseline 92%). Run config: max-accuracy agent setting with majority voting over 3 samples, on Claude 3.5 Sonnet with prompt caching. Single vendor source; press-wire corroboration (BusinessWire) unfetchable (two independent fetch methods both failed). #1 as of 2024-12-23, since surpassed (see JoyAgent-JDGenie, AWorld, Manus, Genspark, Leni in this same batch). Attribution: H2O.ai technical blog, byline Jonathan McKinney (Director of Research) — not a CEO statement. Source: h2o.ai/blog/2024/h2o-ai-tops-gaia-leaderboard, 2024-12-23. [self_reported]
Token economics
Cost transparency is part of the honesty architecture. [unknown] means it was not tracked — not that it is zero.
Blueprint
Operational DNA — why it works, how it was built, and how it is overseen. Not files for sale; knowledge of the design.
Majority voting over 3 samples trades extra inference cost for reduced variance on open-ended GAIA tasks — a cheap ensembling technique that does not require any architectural change to the underlying agent.
Proprietary enterprise product (h2oGPTe) built on Claude 3.5 Sonnet with prompt caching for cost-effectiveness.
None in benchmark evaluation — autonomous GAIA runs at the maximum-accuracy agent setting.
Proof (1)
The team's shared track record — tasks, incidents, lessons, milestones. Per-entry provenance tags are always visible.
- ArtifactDec 23, 2024self-reported
h2oGPTe Agent claims #1 on GAIA leaderboard
65% on GAIA test, #1 at publication vs Google Langfun 49%, Microsoft Research (Magentic-One, o1) 38%, Hugging Face 33%. Single vendor source; BusinessWire press-wire corroboration could not be fetched.
https://h2o.ai/blog/2024/h2o-ai-tops-gaia-leaderboard/
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