Why inference efficiency makes AI agents practical — and what leaders should do next

• TL;DR
• Inference efficiency has dropped dramatically thanks to new GPUs and inference software, turning always‑on, context‑rich AI agents from expensive experiments into deployable systems.
• Code-writing and assistant-style AI now dominate demand signals, pushing enterprises toward vendor-managed stacks because of severe AI talent shortages.
• Measure cost-per-interaction, pilot high-impact use cases, and prefer partners who control inference economics and orchestration.

Think of inference efficiency as the fuel economy for AI agents: when chips and software improve, agents go from concept cars to reliable commuter sedans that companies can actually afford to run 24/7. Recent hardware (NVIDIA’s NVL72 family) and software (TensorRT-LLM and related toolkits) are delivering order‑of‑magnitude improvements in work-per-watt. That changes the math for use cases that need long memory, instant responses, or high transaction volumes — customer support, sales assistants, and developer tools among them.

Quick definitions

Inference: running an AI model to answer a question or take an action (not training).

Context window: how much past conversation or project data the agent can remember and use.

Information-per-watt / inference efficiency: how much useful model output you get for a unit of power — a direct driver of operating cost.

NVL72 / GB200 / GB300: NVIDIA system classes used for inference; GB300 NVL72 is the newer generation, GB200 NVL72 is the prior class referenced in independent tests.

TensorRT-LLM: NVIDIA’s inference software stack tuned for language models, which can dramatically improve latency and throughput.

What actually changed: hardware + software together

Vendor claims and independent tests together tell a clear story: the cost of running inference is falling by factors, not percentages. NVIDIA says GB300 NVL72 achieves roughly 50× more work per megawatt compared with the older Hopper-generation gear — a figure the company frames as roughly a 35× reduction in per-item processing cost. Independent benchmarking of GB200 NVL72 systems (Signal65) found more than a 10× increase in information-per-watt versus older hardware.

NVIDIA’s NVL72 systems are touted as enabling many tens of times more workload per unit of power than previous-generation platforms, cutting per-item processing costs by orders of magnitude.

Software multiplies those gains. Recent updates to TensorRT-LLM produced roughly a 5× performance boost on GB200-class systems for low-latency tasks within months. Community projects and specialized runtimes (tools like Dynamo, Mooncake and language runtimes built for efficiency) continue to compress costs further — so the effective operating expense for persistent agents is dropping quickly.

Why that matters for AI agents and automation

Deploying a persistent agent that keeps project context and answers instantly used to mean either huge cloud bills or unacceptable latency. Lower inference cost changes three levers simultaneously:

• Lower cost-per-interaction — makes high-volume or always-on agents economical.
• More context for the same price — larger context windows without exponential cost increases.
• Lower latency — better customer experience and real‑time developer tooling.

Worked example (illustrative): if historic cost-per-interaction was $0.10, a 10× improvement reduces it to $0.01; a 35× gain brings it to roughly $0.0029. Those numbers turn pilot programs into recurring line items and free budget for richer capabilities (longer memory, multimodal inputs, federated data access).

Demand is following capability

Search and market signals show demand shifting toward agents and code-writing AI. OpenRouter reports that code-writing and assistant-style queries now make up nearly half of AI-related searches, up sharply from about 11% a year earlier. Market forecasts peg the AI agent sector at roughly $4.92 billion in 2024, growing to an estimated $6.02 billion in 2025 and projected to approach $44.97 billion by 2035 (CAGR ~22%).

Platform players are racing to own the experience and economics. Alibaba’s Qwen 3.5 emphasizes lower processing costs and screen-aware, multi-device capabilities; OpenAI has hired the creator of the OpenClaw personal-agent project to accelerate agent product work; and Salesforce reported rapid revenue growth from its agent products — a sign enterprises will pay for managed, integrated solutions.

Talent, risk and why many companies will buy, not build

Technical progress reduces operating cost, but human capital remains scarce. Surveys and studies show a significant AI skills gap — a majority of business leaders report insufficient AI skills internally, global demand for AI talent outstrips supply by several times, and AI roles command a premium in compensation. Most employees learning AI do so on their own time, often self-taught.

That imbalance favors vendor solutions. Historical project success rates indicate vendor-led deployments reach production and ROI far more often than internal builds for most organizations. Given the complexity of productionizing agents (inference, state management, orchestration, security), many companies choose managed stacks to reduce time-to-value and operational risk.

Risks and governance you must consider

• Data residency & compliance — agent memory and orchestration must enforce policies across datasets and jurisdictions.
• Model drift and hallucinations — agents need monitoring, guardrails, and human-in-the-loop logic for critical tasks.
• Vendor lock-in — evaluate portability, export formats for context/state, and multi-cloud options.
• Security — credential handling, injection risks and access control must be architected from day one.
• Cost surprises — without clear cost-per-interaction metrics, long-running agents can balloon cloud bills.

Decision framework: build vs. buy

Use these questions to guide the choice:

• Is the agent core to differentiated IP or customer experience? If yes, consider building selectively; if no, prefer vendors.
• Do you control sensitive data requiring strict residency? If yes, favor solutions that support on-prem or private cloud inference.
• How mature is your ops team for model monitoring, latency SLAs, and cost control? If low, start with vendor-managed deployments.
• What is the acceptable time-to-value? Vendors typically deliver production value faster (months vs. many months or years for internal builds).

Practical next steps and vendor evaluation checklist

Short checklist for leaders evaluating AI agents and AI automation partners:

• Can the vendor provide credible cost-per-interaction estimates based on your expected workload and context window size?
• Do they control inference and orchestration (or partner closely with those who do)?
• What SLAs exist for latency, availability, and data handling?
• How do they handle model updates, monitoring, and human-in-the-loop escalation?
• What portability guarantees exist for agent state, context, and audit trails?
• Can they show customer references with similar scale and compliance requirements?

FAQ

What is inference efficiency and why should I care?
Inference efficiency measures how much useful output you get per unit of power; it directly impacts the operating cost of AI agents. Better efficiency means cheaper, faster, and more context-rich agents.

How much cheaper will agents get?
Benchmarks vary: vendor claims for the GB300 NVL72 range up to ~50× work-per-megawatt (implying ~35× per-item cost reductions), while independent GB200 NVL72 tests show >10× information-per-watt improvements. Expect a practical range of 10×–35× for many workloads today, with continued gains from software and runtime improvements.

Should we build our own agent?
If the agent is core IP, data residency is critical, and you have strong MLOps capabilities, a bespoke build may make sense. For most use cases, vendor-managed stacks deliver faster ROI and lower implementation risk.

Three immediate actions for executives

• Define 1–2 high-value agent use cases (customer support, sales enablement, developer productivity) and estimate cost-per-interaction today vs. after expected efficiency gains.
• Run a 90‑day vendor pilot with clear KPIs (latency, cost-per-interaction, resolution rate, NPS) and an exit clause for portability.
• Invest in a small, cross-functional team to own governance — data, security, and human oversight — while learning from live deployments.

Inference efficiency is collapsing a major economic barrier. The practical question now is less whether agents will matter and more who will own the user experience and the economics. Prioritize measurable pilots, vendor evaluation focused on inference economics, and governance that scales — and you’ll be positioned to turn the efficiency gains into durable business advantage.