Systems | Development | Analytics | API | Testing

Your users increasingly work through AI assistants. When they ask an agent to check a case status, analyze last quarter's metrics, or kick off an approval workflow, that agent needs to access your enterprise systems. Enabling that connection is the core challenge of AI agent integration: giving AI assistants the ability to discover, understand, and safely interact with business applications and data on behalf of users.

Organizations have rapidly adopted artificial intelligence, but a stark divide is emerging: those who are embedding AI into the core of their operations, and those who are treating it as a standalone tool. According to a recent Harvard Business Review Analytic Services survey, only a small share of resondents say their organization has largely integrated AI into workflows.

In 2024, two of the largest data platform companies, each with billions in revenue and dedicated AI research teams, invested in building their own foundation models. One spent roughly $10 million training a 132-billion parameter model on 3,072 NVIDIA H100 GPUs. The other released a 480-billion parameter model optimized for enterprise tasks like SQL generation and code. Both achieved strong results within their compute class.

In my younger days, eons ago in tech years, I worked on many enterprises IT projects or saw them up close. Failure rates of these projects were incredibly high. There was a mortgage system that was expected to be live in six months but ended up taking over five years and went live with a small fraction of the features originally planned. Many other projects never got out of the development phase.

When we develop applications, we sometimes only focus on the “how”—how to build the processes, how to architect the data structure, and how to encode the correct logic. But for users, the "what" is their reality—and sometimes that’s overlooked during development. An application that looks and feels like your brand identity isn't just visually appealing. It builds trust, reduces cognitive load, and makes your application more enjoyable for your users.

Have you ever launched an enterprise application that sailed through every baseline test, only to falter when confronted with real-world demand? When you’re modernizing critical workflows for a major financial institution, a “good enough” architecture is a ticking time bomb. In high-volume operations, performance failures aren't just minor setbacks—they halt transactions, stall back-office teams, and expose the business to significant operational risk.

As enterprise development teams scale, coordinating deployments across multiple teams, applications, and environments becomes one of the most time-consuming parts of the delivery lifecycle. Today, we're excited to introduce Releases—a new capability in Appian that brings native release management to the platform, helping teams deploy faster and with fewer surprises.

AI is at the center of every conversation around operational efficiency, while at the same time being sidelined. In a recent Harvard Business Review Analytic Services survey, only 18% of organizations report that AI is integrated within most of their workflows; twice as many run it as a standalone tool alongside the work. That gap—between AI that assists and AI that operates—is the defining problem of enterprise AI agents.

Astera today announced the launch of Centerprise AI, the agentic evolution of its enterprise data management platform. Centerprise AI embeds proprietary agentic harness across the full data management stack, enabling data teams to design, test, and deploy their data assets, warehouses, pipelines, data models, and analytics in a single platform.