French technology startup Kog is developing a specialized software layer designed to optimize graphics processing unit (GPU) performance, challenging the industry consensus that current hardware architectures are fundamentally ill-suited for agentic AI workflows. By refining the interface between hardware and software, Kog aims to maximize inference efficiency and reduce the computational overhead associated with autonomous AI agents.
The company’s strategy focuses on “going deeper” into the GPU’s operational mechanics to extract higher performance from existing hardware. This approach targets the specific inefficiencies that arise when AI models move beyond simple prompt-and-response interactions toward agentic behavior—processes that require iterative reasoning, multi-step planning, and the frequent use of external tools.
The Technical Challenge of Agentic Inference
Traditional LLM inference is largely linear: a user provides an input, and the model generates a sequence of tokens until a stopping point is reached. However, agentic workflows operate in loops. An AI agent must analyze a goal, formulate a plan, execute a step (such as searching a database or running code), observe the result, and then re-evaluate its next move.
This iterative cycle creates significant bottlenecks in current GPU utilization. Each loop often requires the model to re-process large amounts of context, leading to high latency and inefficient memory usage. The prevailing narrative among many hardware developers has been that these “looping” workflows require entirely new chip architectures or massive increases in memory bandwidth to be commercially viable at scale.
Kog is contesting this premise. Rather than waiting for a generational shift in hardware, the startup is building optimization tools that change how the GPU handles these iterative tasks. By optimizing how data is cached and how the GPU manages the state of an ongoing agentic process, Kog intends to squeeze significantly more inference capacity out of the chips already deployed in data centers.
Why Optimization Matters for the AI Ecosystem
The push for software-level optimization is driven by the escalating costs of AI infrastructure. As enterprises shift from simple chatbots to autonomous agents capable of managing complex business processes, the demand for compute is growing exponentially.
If the industry remains dependent on the “brute force” method of adding more GPUs or migrating to expensive, specialized Application-Specific Integrated Circuits (ASICs), the cost of deploying agentic AI may remain prohibitively high for all but the largest technology firms.
By unlocking latent capacity in existing GPU infrastructure, Kog’s approach could democratize the deployment of autonomous agents. If software can reduce the compute required for a single agentic loop, companies can run more agents on the same hardware, reducing the total cost of ownership and decreasing the time it takes for an agent to complete a complex task.
Analysis: Shifting the Bottleneck from Hardware to Software
Kog’s strategy represents a strategic bet on software efficiency over hardware replacement. For the past several years, the AI narrative has been dominated by the “compute war,” where the primary metric of success has been the number of H100s or B200s a company can acquire. This has created a dependency on a small number of hardware providers and a belief that performance gains are primarily a function of silicon.
By focusing on the hardware-software interface, Kog is attempting to shift the bottleneck. If their optimization layer succeeds, the constraint on AI scaling moves from hardware availability—which is subject to supply chain volatility and extreme capital expenditure—to software ingenuity.
Furthermore, this approach addresses the “latency gap” in agentic AI. For an agent to feel autonomous and responsive, it must be able to iterate through its reasoning loops in milliseconds. Hardware upgrades provide a linear increase in speed, but algorithmic and interface optimizations can often provide exponential gains by eliminating redundant computations. If Kog can successfully minimize the overhead of the agentic loop, they effectively increase the “intelligence per watt” of the existing global GPU fleet.
Context: The Rise of Agentic AI
The industry is currently transitioning from “Generative AI” to “Agentic AI.” While the former focuses on content creation, the latter focuses on goal execution. Agentic systems are designed to operate with a degree of autonomy, using a “reasoning-acting” cycle.
This transition has exposed the limitations of current inference engines. Most existing systems are optimized for throughput (how many tokens can be generated per second) rather than agility (how quickly a model can pivot its reasoning based on new data). This gap has led to the current exploration of “inference-time compute,” where models are given more time and computational resources to “think” before they respond. Kog’s work sits at the center of this trend, attempting to make that “thinking time” as efficient as possible.
What to Watch Next
As Kog moves forward with its optimization methods, several key indicators will determine the viability of their approach:
1. Benchmark Performance: The industry will be looking for empirical evidence that Kog’s software can significantly reduce latency in multi-step agentic loops compared to standard GPU configurations.
2. Hardware Compatibility: A critical factor will be whether Kog’s optimizations are proprietary to specific GPU architectures or if they can be applied across a broader range of hardware, including different generations of NVIDIA chips or competing accelerators.
3. Integration with Frameworks: For Kog to achieve widespread adoption, its technology must integrate seamlessly with the frameworks developers already use to build agents, such as LangChain, AutoGPT, or proprietary enterprise orchestration layers.
4. Response from Hardware Giants: If software optimization proves highly effective, it may influence how hardware manufacturers design future chips, potentially shifting focus from raw power to more flexible memory management and state-handling capabilities.
Conclusion
Kog is positioning itself as a critical bridge between the current state of GPU hardware and the future requirements of autonomous AI. By challenging the notion that agentic workflows require a total hardware overhaul, the startup is pursuing a path of efficiency that could lower the economic and technical barriers to AI autonomy. If they can successfully squeeze more inference out of existing GPUs, the path to scalable, affordable agentic AI may lie not in the silicon itself, but in the code that directs it.
Sources:
TechCrunch: https://techcrunch.com/2026/08/14/kog-is-going-deeper-to-squeeze-more-inference-out-of-gpus/
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Story synopsis gathered from: TechCrunch — source
