Enterprise AI agents are moving into work that demands more than a convincing demonstration. These systems can trigger workflows, access sensitive data, and make operational decisions across an organization, so teams need ways to validate their behavior before deployment.
That shift is driving a new group of infrastructure tools focused on safe runtimes, architecture, governance, and test data. The shared message is simple: building an agent is becoming easier, but proving that it can be trusted with a real business process requires much more than reviewing its code.
From coding agents to trusted systems
Simon Willison, author of the weblog post, highlights a core skill for working with coding agents: people must confidently instruct agents on the changes they need and then verify those changes. Eyeballing every line of code has never been the most effective way to validate software, and that challenge becomes harder when an agent can create or modify work across a larger system.
Teleport explains that safe AI deployment starts with an isolated ephemeral trusted runtime. That approach gives an agent a controlled place to operate before it reaches live systems, creating a boundary between the agent’s actions and sensitive business environments.
The need for that boundary grows as agentic AI implementations move past chatbot pilots. Pilot-era stacks often focused on quick wins, but they were not built for enterprise standards covering reliability, security, governance, cost control, and vendor sustainability. As adoption scales, organizations face integration brittleness, runaway costs, stale or untrusted data, and governance gaps.
Info-Tech Research Group has published its Discover the Enterprise Agentic AI Technology Stack blueprint to help IT teams assess architecture gaps and prepare for vendor evaluation. The research reflects a marketplace that now spans a broad and expanding technology stack, from application tools to infrastructure.
A broader architecture for enterprise agents
Teams without a clear architecture can end up with overlapping vendors, gaps in control, and little insight into what their agents are doing. Budget often flows toward novel capabilities, while foundational elements such as observability, governance, and integration reliability receive less attention.
The blueprint divides the enterprise agentic AI technology stack into six layers:
- Application
- Data and AI lifecycle management tools
- Foundational models
- Agentic execution and orchestration engine
- Data platform
- Infrastructure
Those layers describe more than where an agent runs. They also show where organizations must manage data, models, workflows, controls, and the systems that support production use. A stack that works for a pilot may leave important questions unanswered once an agent starts acting across the enterprise.
Bill Wong, AI research fellow at Info-Tech Research Group, captures the difference between a demonstration and a dependable system: “Agent demonstrations look alike, but operational realities do not.” He says vendors worth betting on are those that make agents easy to observe, explain, debug, govern, and remove safely.
Info-Tech’s six vendor selection criteria are functional use cases; operability and production reliability; deployment compatibility and flexibility; governance, security, and compliance; integration and ecosystem fit; and implementation and operational costs. Together, the criteria give IT teams a way to judge the full operating model instead of focusing only on an agent’s visible features.
Andrew Kum-Seun, research director at Info-Tech Research Group, describes the long-term goal this way: “The most critical architectural decision an IT leader can make is building a technology stack designed not for today’s answers, but for tomorrow’s unknowns.”
Testing agents with realistic business conditions
Synthesized is addressing another part of the problem with its Test Data Agent, which creates and provisions realistic data, business context, and system states for validating AI agents before deployment. The tool integrates with agent development, evaluation, testing, and orchestration frameworks.
The Test Data Agent helps teams identify the data and system states an agent needs, generate or mask production-representative data, and create repeatable scenarios. It operates within on-premises, private-cloud, and hybrid environments under existing security controls, making it designed for enterprises with highly sensitive and regulated data estates.
The tool has a particular use in complex SAP environments. Specific use cases include pre-production validation, SAP ECC to SAP S/4HANA migration validation, regression testing, application modernization, privacy-safe environments, and continuous validation.
That focus matters because an agent’s performance depends on the conditions around it. An evaluation framework can measure an agent’s actions, but a useful test also needs realistic data, business context, and system states that reflect the environment where the agent will work.
Nicolai Baldin, Founder and CEO of Synthesized, puts the challenge plainly: “Building an agent is becoming easier. Proving that it can be trusted with a real business process is not.” He adds, “Evaluation frameworks can measure how an agent performs, but they still need a realistic world in which that performance becomes meaningful. The Test Data Agent is built to create that world safely, before an agent is allowed to act on live systems.”
Synthesized positions the Test Data Agent as an open infrastructure component for the enterprise agent ecosystem. It is already in early access with tier 1 global bank design partners, giving the product an initial setting where regulated data, repeatable testing, and control over deployment conditions matter.
Taken together, the new tools point to a change in how enterprises should judge AI agents. Code review remains useful, but trusted deployment also requires an isolated runtime, realistic test conditions, clear architecture, observability, governance, and a safe way to remove an agent when its work no longer meets expectations.
Based on
- More than just code review — simonwillison.net
- Pilot-Era Agentic AI Stacks Expose Enterprises to Integration and Governance Risks, Finds Info-Tech Research Group | National Post — nationalpost.com
- Synthesized Introduces Test Data Agent, Bringing Production-Faithful Validation to Enterprise AI Agents | Markets Insider — markets.businessinsider.com
