Originally published at: Optimizing Data Center Performance with AI Agents and the OODA Loop Strategy | NVIDIA Technical Blog
For any data center, operating large, complex GPU clusters is not for the faint of heart! There is a tremendous amount of complexity. Cooling, power, networking, and even such benign things like fan replacement cycles all must be managed effectively and governed well in accelerated computing data centers. Managing all of this requires an accelerated…
Hope anyone here finds this useful. Happy to answer any questions about our approach, the models involved, and the challenges we’ve had along the way to making this possible.
Hi Aaron,
Great to see Nvidia embracing Agentic OODA! Back in the '80s, OODA was developed for AI robotics, but compute was not ready.
We have a new worldwide OODA Research circle centered on UTexas Humanoid Robotics (AE, CS, ME, EE) and IEEE-RAS. Several of us are in Nvidia’s Developer Community. How can we collaborate?
TIA.
Dave
Texas
Would be happy to collaborate on this, very fascinating what you are working on.
Great project with great applicability across many domains and industry verticals. I lead Edge AI architecture team at Verizon and we are exploring, together with partners, building blueprint GenAI architectures for different use cases that benefit from using 5G networks to ingest the swath of sensor data required to enable such use cases. Industrial systems/workflows observability is a common and low hanging fruit use case I see across our customer/prospect base. This particular POC could easily be adapted to address such use case - the architectural patten is very applicable. would be glad to connect offline and explore possible avenue for collaborations.
Thanks Aaron,
The group is excited over your astute identification of Agentic OODA now that compute is scaling up to the potential. We will dive deep into your suggestions and stay in touch.
A follow-up graphic documenting due-diligence review of major prior Cognition Schemas, then “OODAfied” (conformally mapped to OODA). OODAfication facilitates identifying commonalities and unique features across cognitive models, while dispensing with nonessential features.
Anyone interested in helping develop an early OODA-based reference standard for a Foundation Agent Architecture, to especially integrate NIVIDIA hardware, and NVIDIA GR00T, GEAR, and Isaac software components, to create leading Frontier Agents, from Data Centers to Humanoids, for power, efficiency, and secure interpretability, from CUDA-object to AGI-ASI deliberation, please reach out here or directly to folks.
More updates pending.
OODA has military roots first in fighter jet dogfighting, then nuclear warfare strategy, paired to “C2” Command and Control (paradigm) operating up to theatre scale. The prior graphic showed academic Cognition Models made conformal to OODA. These are military versions, including OODA itself, as it first emerged to civilian engineering-science notice.
No need to human-conformalize more non-OODA models, enough examples exist to train automated conformance (Visual Prompting).
Agentic OODA has suddenly gone viral in LinkedIn, quite apart from its small longtime specialist culture.
@AaronErickson Will you be able to open source this for us to reuse for other use cases?
Enough review of legacy Agentic Cognition Models, and on to current Agentic OODA challenges.
Foremost is operationalizing OODA scalably, starting from the CUDA object abstraction level. This calls for extending CUDA to higher symbolic semantics in what is being called SymCUDA, proceeding wonderfully thanks to AI help.
The top-down theoretic challenge has been fitting rich messy Natural Language to the crystal-order of GPU processing. This ideally requires OODA tokens to be exactly regularized to 32 threads in GPU warps by translating Natural Language into decluttered “Control Action Language” semantics.
The emergent vision is an “Enchanted Loom” of nestled (fractal loop-in-loop) OODA Phases, with Lattice Waves of data processing washing across GPU dies. This dynamic would be scale-free from a single Edge GPU to entire Data Centers, and even clusters of DCs, as tiny or vast OODA whirlwinds.
These hardware-optimized OODA patterns would be contained in Kubernetes Nodes managed at the Enterprise Level of OODA abstraction (NVIDIA/TrueFoundry llo11ypop). This may prove the most apt path to SGI compute in the 2026 timeframe.
I am expecting some announcements at GTC around this topic… stay tuned.
Background OODA ASI DC research workflow is now highly AI driven, after decades of slow human architecting toward Newell’s vision of a Unified Cognition Model.
Now early defining Kubernetes OODA DC Operators over hybrid Cloud-on-Prem. Kubernetes Operators explicitly being cybernetic Control Loops, their OODAfication is straightforward. OODA itself is expanding to an Actor-Critic dual OODA dataflow for various theoretic advantages. Fast OODA-on-prem could interact with ubiquitous OODA-in-cloud, in a synergistic multi-dynamic inherently supported in Kubernetes.
Stargate hyperscale DCs are the default design target in the 2025-30 timeframe. OODA ASI at DC cluster hyperscale could even shape physical layout of Stargates in a hub/spoke/ring topology. The hub would support orchestration via the spoke paths, but the massively parallel optical ring would be a real ASI kicker, able to pass entire DC-scale images from OODA DC node to DC node in eyeblink lockstep.
To have something apt to run on OODA ASI, starting to define ASI applications to run on Stargate OODA ASI, like curing disease, and revolutionizing science and technology, and even gaming.
Security continues to be concerning, not so much that hostile actors may steal or disrupt, but that Agentic ASI could outsmart us and go rogue or be monopolized by bad human governance. Kubernetes Containerization is a basis for firewalled LLMs to behave yet scale up to vast numbers, as unit-LLMs already seem to be reaching unit-scaling limit tradeoffs.
A thousand pages or so of dense OODA ASI context is being distilled to dump into an AGI LLM hopper by this Fall, to see what happens.
Nvidia’s NeMo architecture is developing topologically conformal to OODA. K8s OODA Operator YAML setup files now iterating in drafts. More work needed to define high dimensional Current and Desired Agentic System States for Control Loop flywheel action, so that hyperscale fractal 4 or 8bit RL looping of thousands of State variables in GPU, Tensor Core, or TensorRT is as fast and simple as possible.
Elsewhere in NVIDIA forums-
Supercharge Agentic AI with Scalable Data Flywheels/ Technical Blog - NVIDIA Developer Forums
Update: OODA has proved an ideal extensible baseline schema for integrating prior 5thGen Cognitive Models. Conforming and mapping prior models to neoOODA is complete. Meanwhile, 6thGen AI has redefined DC Data Fabric, and a next generation of hyperscale Data Centers are being defined. The processing stack from GPU die and CUDA to neoOODA is firming up.
An Optical Interconnect Layer in DC Data Fabric has been conceived, that embeds the neoOODA abstraction layer, using Docker and Kubernetes, with a Kube Operator doing the fancy logic. Topology-on-Demand (ToD) is a critical challenge, as refinement of SDN (software defined network). A modern DC must be able to instantly switch topologies on demand, from Online Server to Science Supercomputing to Agentic OODA and so on, matching compute to load demand topologically. Only an Optical Switching Layer can do this properly.
OODA is emerging as the leading formal ASI paradigm, having started soundly developed 70s years ago to automate Cold War Game Theory. Anyone not doing OODA for ASI must “reinvent the Wheel”. The AI Cowboys Consortium of Texas AI players is positioning to participate in the trillion-dollar Stargate DC buildout, based on Nvidia hardware. OODA ASI under ToD will is in draft specifications. More news soon.