The Reality of Agent-Driven Technology Evaluations
AI agents are showing up everywhere: in code editors, workflow tools, data platforms, and research environments. When it comes to Technology Landscape Assessments (TLA), it is tempting to assume they’ll make technology evaluations faster and easier. But for data-driven organizations, the reality is more complicated.
When you’re dealing with sensitive data, regulated workflows, or complex scientific pipelines, AI agents don’t remove the hard parts of a TLA, they shift where the hard parts live.
The New Bottleneck: Data Sovereignty, Not Discovery
Our new white paper comparing traditional TLAs with Agentic TLAs (ATLA) argues that the real focus should not be on using agents to analyze code and data, or summarize documentation. They can and they are fast. Minutes instead of hours or days. The challenge is where they’re allowed to run and what data they’re allowed to see.
- Some workflows can safely use cloud‑based agents.
- Others require tightly governed hybrid access.
- And the most sensitive environments demand full on‑premise isolation.
A one‑size‑fits‑all agent architecture simply doesn’t work.
A White Paper for Understanding Agentic TLAs
To help teams navigate this, we are developing an Agentic Landscape Assessment Framework, a practical framework for evaluating technical systems, data flows, and governance requirements that can be optimized with AI agents across any project, lab, program, or platform. The white paper covers the high-level considerations in developing the framework and the human-in-the-loop (HITL) responsibilities.
It helps you answer questions like:
- When is an AI agent appropriate?
- What sovereignty tier does a workflow require?
- What infrastructure must be in place before agents can help?
- Where are the compliance risks hiding?
- What human skills are needed to be the HITL?
It’s not about replacing human expertise. It’s about giving teams a structured way to decide how to deploy agents responsibly and efficiently.
Who This Is For
If you’re modernizing a research workflow, building a new data platform, or trying to understand how AI fits into your technical environment, this white paper is written for you.
Read the Full White Paper
If you want a high-level, clear, grounded explanation of how AI agents change the landscape, and how to evaluate your own environment, the white paper provides a useful introduction to the framework.
→ Read the white paper