Over the past year, it’s become increasingly clear that the most consequential change in AI isn’t happening in models — it’s happening in volume. As agentic systems move from experimentation into production and autonomy, autonomous activity is beginning to rival, and poised to exceed, human-driven traffic across the internet. That shift changes the economics and the physics of software.

For decades, data infrastructure has evolved around human interaction patterns: bursty demand, predictable peaks, and long periods of relative calm. Agentic AI breaks those assumptions. Agents operate continuously, coordinate with one another, and exert constant pressure on APIs and databases as they plan, retry, and adapt at machine pace. What was once an edge case becomes the steady state.

As this transition accelerates, scalability and reliability converge into a single, non-negotiable requirement. Systems of record can no longer be merely durable or fast enough — they must remain available, consistent, and cost-efficient under unrelenting, autonomous load. The AI era won’t be defined by what systems generate, but by whether the infrastructure underneath them can survive success.

Agentic AI is dismantling your infrastructure playbook

Agentic AI marks a shift in how applications operate, not just how they behave. Software is shifting from responding to human requests to running continuously, as autonomous systems pursue goals, coordinate actions, and adapt in real time. That shift fundamentally alters what infrastructure must support.

For decades, systems were designed around human scale: predictable peaks, idle periods, and the assumption that pauses and inconsistencies were tolerable. Agentic workloads break those assumptions because they are hitting the database far more frequently than human users, and they never rest.

As a result, risk has quietly shifted. AI systems rarely fail because models collapse; they fail when the foundations that manage state, rules, and coordination can’t keep up. The impact shows up subtly: slower decisions, inconsistent behavior, and rising cost, long before anyone declares an outage.

At machine pace, delay becomes indistinguishable from failure. Even small gaps in availability or freshness compound quickly, eroding trust before dashboards turn red. The infrastructure playbook that powered earlier generations of software simply wasn’t written for this world and agentic AI is making that impossible to ignore.

Four failure patterns companies are experiencing

These same four failure patterns keep cropping up across the industry:

All of these affect both performance and cost. While GPUs draw the most attention, fragmented data plumbing often becomes a major driver of inefficiency—increasing infrastructure spend and limiting how effectively AI compute can be used.

The architecture AI Innovators really need

The architecture AI Innovators actually need centers around availability, consistency, scalability, and locality:

• Global availability by default: AI systems operate continuously across regions. The data platform must remain available through failures, upgrades, and topology changes—without downtime or application rewrites.

• Strong, serializable consistency: Agents depend on a coherent view of reality. Shared state must remain correct under concurrency; eventual consistency is no longer sufficient for autonomous decision-making.

• Elastic, horizontal scale: Agentic workloads are unpredictable and fast-moving. Systems must scale reads, writes, and storage together—up or down—without sharding, migrations, or overprovisioning.

Local data placement for compliance and speed: Global AI requires locality without fragmentation. Data must stay in-region where required, while remaining accessible with low latency to agents and models worldwide.

CockroachDB: A Single Operational Foundation for AI

CockroachDB is a globally distributed operational database built to support AI systems from early prototypes through autonomous production. It unifies transactional data, vector embeddings, and agent state in a single, always-on platform, allowing AI applications to scale and evolve without losing correctness, availability, or control. With strong consistency, global resilience, and elastic scale built in, CockroachDB helps teams bridge the gap between AI ambition and operational reality.

The Postgres you know—redefined and and upleveled for global AI

CockroachDB combines horizontal scaling with PostgreSQL compatibility in a familiar interface where Postgres users can become productive almost immediately. Existing ORM layers, SQL skills, and operational patterns transfer, transitioning smoothly to a globally distributed platform. This empowers platform teams to modernize the operational data layer without forcing each member to learn a new paradigm.

Single source of truth: Operational plus vector

CockroachDB provides a single operational data platform that handles transactional workloads and vector data. This closes consistency gaps, reduces security surfaces, and ensures that AI deployments are always acting on the freshest data; and the same, strongly consistent “ground truth” your core business logic uses.

Performance under Adversity (PuA) ensures continuous availability

CockroachDB Performance under Adversity (PuA) testing demonstrates the ability of the platform to thrive when adversity strikes—in fact, when multiple worst-case examples of adversity strike. PuA testing shows how CockroachDB maintains stability and functionality throughout a series of real-world challenges including backups, change feeds, schema changes, disk stalls, network failures, node restarts, zone outages, and regional outages. The system automatically rebalances replicas and routes traffic around failing nodes or regions, keeping the cluster available without manual intervention. Which is why we can confidently discuss how CockroachDB customers were affected by recent major global outages: They weren’t.

Built-in audit, time travel, and change data capture (CDC) for traceability

Agentic systems operate at machine pace, not human pace. Without native traceability, you can’t explain agent decisions, audit behavior, and potentially most costly of all, you can’t meet regulatory or internal governance expectations. CockroachDB treats traceability as a first-class operational property, not an afterthought, making it ideal for always-on, autonomous, globally distributed AI systems. The platform lets you reconstruct an agent’s behavior end-to-end, from identifying the agent or user (audit), to recreating the exact data context (time travel), to replaying the resulting effects (CDC).

Why this matters now

AI investment is moving out of the experimental phase and into operational accountability. Leaders are being asked to prove measurable improvements in uptime, resilience, and cost, not just model metrics. 

This is where infrastructure choices start to matter. As AI systems become autonomous and always-on, the operational data layer determines whether they can function continuously under pressure or quietly degrade when demand spikes or conditions change. What once felt like a background technical decision now sits squarely on the critical path.

The data platform may not be the most visible part of the AI stack, but it is the one that ultimately decides whether systems can endure growth, adapt safely, and operate at global scale. CockroachDB represents a pragmatic path forward: Postgres semantics, global availability, strong consistency, vector-native capabilities, and zero-downtime operations all in a single unified platform.

Learn more about how top AI Innovators are achieving success with CockroachDB.

Jeff Cotrupe leads analyst relations at CockroachDB and contributes in other areas including customers, community, and strategy. He’s a MongoDB, Gartner, SingleStore, and Auburn alum with decades in data who operated his MarketPOWER+ LLC for 5+ years.