Office of the CTO

AI App Delivery Top 10: Unoptimized traffic steering

AI breaks traditional traffic steering assumptions: backends aren’t interchangeable, costs vary, and agents fan out unpredictably—so routing must account for model, GPU health, and data locality.

Your next SRE might be a machine

Control is concentrating at the point of intent: prompts, tokens, and routing. The architecture challenge is defining where AI can act, when it must defer, and how to reverse.

AI App Delivery Top 10: Insufficient Traffic Controls

Traditional rate limits and throttles miss the real blast radius. AI workloads fan out across models, vector stores, and services, driving latency spikes, cascading failures, and retries.

AI App Delivery Top 10: Incomplete observability

AI won’t scale by itself. Without a unified control plane that collapses tooling and ownership boundaries, inference becomes the most brittle and coordination-heavy tier.

The secret to scaling AI is overcoming Conway’s Law

AI won’t scale by itself. Without a unified control plane that collapses tooling and ownership boundaries, inference becomes the most brittle and coordination-heavy tier.

AI App Delivery Top 10: Lack of fault tolerance and resiliency

AI makes classic resiliency gaps far more costly: single GPU or dependency failures cascade through synchronous inference chains, compounding latency and degrading outputs.

AI App Delivery Top 10: Weak DNS practices

For inference and agentic systems, DNS resilience is critical to availability and performance—bad resolution means misrouting, latency spikes, and service blackouts across regions.

Behavior and boundaries: The agentic security shift

Agents create emergent, unbounded sequences where risk accumulates over time. Security must shift from single-request validation to continuous behavioral governance across multi-step, evolving flows.

Identity is the gatekeeper to agentic AI

As agentic AI shifts machines from tools to actors, identity becomes the core control: who can act, on whose behalf, with what scope—enforced inline at inference time.

AI is driving the emergence of new traffic types

AI adoption is creating new first-class traffic types: inference requests plus machine-driven automation traffic and high-volume telemetry traffic that feed control loops.

Inference is now the center of operational gravity

Multi-model operations and API-driven automation push control to inference-time layers—prompt, token/control, routing, and moderation—where cost, risk, and performance must be managed.

Sessions are sticky, context is clingy: How inference cheats to maintain conversations

“Stateless” inference isn’t truly stateless—conversation state is hauled along in tokens each request. That replay drives bandwidth, compute, and latency as context grows.

Agent skills: An emerging open standard

Skills declare intent but don't enforce control. Real security must live at the tool boundary, where agent thoughts become actions and policy can be enforced.

The new hybrid multicloud challenge is operating without an app delivery and security platform

Enterprises aren’t leaving on-prem—they’re modernizing it. As portfolios stay mixed, app delivery and security must shift from environment-specific features to a unified platform.

From packets to prompts: Inference adds a new layer to the stack

Inference is not training. It is not experimentation. It is not a data science exercise. Inference is production runtime behavior, and it behaves like an application tier.

Compression isn’t about speed anymore, it’s about the cost of thinking

In the AI era, compression reduces the cost of thinking—not just bandwidth. Learn how prompt, output, and model compression control expenses in AI inference.

The efficiency trap: tokens, TOON, and the real availability question

Token efficiency in AI is trending, but at what cost? Explore the balance of performance, reliability, and correctness in formats like TOON and natural-language templates.

Programmability is the only way control survives at AI scale

Learn why data and control plane programmability are crucial for scaling and securing AI-driven systems, ensuring real-time control in a fast-changing environment.

The top five tech trends to watch in 2026

Explore the top tech trends of 2026, where inference dominates AI, from cost centers and edge deployment to governance, IaaS, and agentic AI interaction loops.

Multicloud chaos ends at the Equinix Edge with F5 Distributed Cloud CE

Simplify multicloud security with Equinix and F5 Distributed Cloud CE. Centralize your perimeter, reduce costs, and enhance performance with edge-driven WAAP.

The State of Application Strategy: What’s Really Going on with Multicloud?

The Operational Experience Crisis

Insights from the State of Application Strategy Report 2025

The Application Delivery Top 10

App delivery patterns for scaling apps and APIs

ADC10 Poor Resource Utilization

ADC09 Bespoke Application Requirements

ADC08 Lack of Security and Regulatory Compliance

ADC07 Incompatible Delivery Policies

ADC06 Inability to Handle Latency

ADC05 Unoptimized Traffic Steering