“Agentic AI” is rapidly becoming one of the most commonly used terms in AI discussions, but it is increasingly being applied to very different technologies.
In language services, conversations about agentic AI often conflate three distinct categories:
- humans using AI tools,
- automated workflows that incorporate AI, and
- genuinely agentic systems.
These models solve different problems, rely on different architectures and require different forms of governance.
This distinction matters because agentic systems may address a long-standing trade-off in language services as, historically, automation has delivered efficiency through standardization.
Faster turnaround times, higher throughput and lower costs were achieved by defining processes in advance and applying them consistently.
The more automated the process, the less flexibility there was to accommodate different content types, quality requirements, regulatory obligations or organizational preferences. Organizations frequently had to adapt to the workflow rather than the workflow adapting to them.
The 3 AI Operating Models
- The first operating model is the one most organizations are already familiar with. AI supports human decision-making through capabilities such as machine-assisted translation, drafting, terminology management or quality recommendations. The objective is productivity, but responsibility for decisions and outcomes remains with the individual using the system.
- The second model embeds AI within automated workflows. Translation management systems, workflow engines and business rules route content, trigger actions and execute predefined processes, including calling AI tools (MT, QE-APE) to fully or partially automate some steps. Additional layers of automation may be introduced through robotic process automation or similar technologies, but the underlying principle remains unchanged: the system follows instructions defined in advance. The objective is consistency, throughput and scale.
- The third model is where genuinely agentic systems emerge. Rather than executing a predefined sequence of actions, the system is given an objective and determines how best to achieve it within defined boundaries. It may select tools, choose task sequences, decide when to escalate and adapt its behaviour according to the context. This represents a different architectural approach. Agentic systems introduce an intermediate layer of reasoning that allows actions to be selected dynamically in pursuit of a goal.
Governance Maturity is Required to Match Technological Maturity
The underlying question across all three operating models is the same: how does an organization ensure that decisions are made in accordance with its requirements? The answer evolves as decision-making authority moves through the system. In AI-assisted workflows, governance sits primarily with people. In automated workflows, governance is expressed through business rules, process controls and approvals. In agentic systems, governance must be sufficiently mature that autonomous decision-making can take place safely within it.
For that reason, agentic AI is as much a governance journey as it is a technology journey.
The challenge is not simply building more capable agents. It is ensuring that organizational requirements are defined clearly enough, controls are robust enough and accountability mechanisms are visible enough for those agents to operate reliably. The higher the autonomy, the greater the governance maturity required to support it.
Even today, the handoff of certain tasks to AI within the second model requires an enhanced, digital approach to governance. Preparing for an agentic future therefore starts long before the first agent is deployed. Organizations need a way of defining terminology, quality standards, regulatory requirements and risk tolerances so that these can be applied consistently. They need visibility into decision-making, clear escalation paths and auditable controls.
At TDL, our approach to building technological and governance maturity hand-in-hand is through the combination of AI Profiles (governance), the STREAM AI orchestration layer (technology) and Managed AI services (implement, run, optimize).
Conclusion
The discussion around agentic AI often focuses on models, agents and architectures. These are important, but they are not the common thread. The common thread is governance. As decision-making moves from people, to workflows, to agents, the governance required to support those decisions must mature alongside it. Organizations that succeed in an agentic future are unlikely to be distinguished solely by the sophistication of their technology but by the maturity of the governance that underpins it.
