The more powerful a system becomes, the more important the framework it operates in. The more that is technically possible, the more the user is obliged to define precisely what is actually wanted in operation.
01 The problem
Agentic AI: tempting, yet irresponsible?
Artificial intelligence is increasingly taking over legal workflows. Agentic AI promises the next step: systems that pursue goals independently, make decisions and carry out tasks without a human having to intervene constantly. At first glance, this looks remarkably similar to the way people work.
In legal case handling, however, the idea of autonomy runs up against a fundamental principle: legal processes are not scientific optimisation problems but chains of responsibility.
Three questions show why this is a difference that matters:
- Who decided, and who bears responsibility for it? With autonomous systems, the chain between action and decision-maker becomes blurred.
- At what point in time? In law, the exact timing of an action matters – procedural acts are bound to deadlines and dependencies.
- With which alternatives? At its core, legal case handling is risk management. Weighing options requires clearly defined parameters and individual interests, not autonomously generated assumptions.
02 The core problem
A lack of determinism
The core problem of agentic AI in a legal context is the absence of determinism. Autonomous agents act in a context-dependent, adaptive and probability-based way. If a system handles the same set of facts differently today than yesterday, that is not progress in law but a risk – and one that is unacceptable in an enterprise environment.
Three principles follow from this almost inevitably:
- Deterministic procedures – Legal processes need predictable, rule-based procedures rather than probability-based decisions.
- Predictable escalation paths – Exceptions must be clearly defined and escalated in a traceable way, not generated adaptively by the AI itself.
- Clearly defined decision points – The boundary between human and machine must be modelled explicitly, not left to the chance of probabilistic systems.
03 The fallacy
More intelligence ≠ less control
A widespread fallacy goes like this: the more intelligent a system, the less steering it needs. Missing workflows and scattered, poorly maintained data sources are then supposed to be bridged by particularly powerful AI agents.
Many discussions about legal AI miss the real question. It is not the intelligence of a model that determines success or failure, but the architecture it is embedded in.
Autonomous systems shift governance into the black box. The resulting loss of control is not acceptable from a regulatory perspective and is highly risky from a business perspective. – Tim Platner, Managing Director of Legal Data Technology GmbH
04 The comparison
Agentic AI vs. workflow orchestration
The alternative to autonomous AI is not to do without AI, but to embed it in an orchestrated way. Two fundamentally different approaches pursue the same goal:
| Autonomous systems – agentic, adaptive, uncontrolled | Orchestrated systems – structured, explainable, controllable |
|---|---|
| Decision-making process cannot be explained or reproduced | Deterministic, auditable processes |
| No systemic limits | Complete logging of all steps |
| Governance in the black box | Enterprise-ready and compliant |
| Chains of responsibility become blurred | Controlled and precise integration of AI |
| High liability risks in case handling | Decisions traceable and controllable |
05 AI in workflows
AI as an instrument, not as the conductor
The solution is not to do without AI, but to integrate it in an orchestrated way. Here, agentic AI operates within clearly defined roles and limits: it does not decide whether a process step takes place, but how well it is carried out within the given framework.
- Processes – explicitly modelled, versioned and binding for everyone involved; every step is fixed.
- AI – highly precise within defined limits, in service of the overall process.
- Orchestration – DEPLAW acts as an operating system that controls timing, escalation and responsibilities in real time.
06 The solution
Why BPMN and explicit process models win
BPMN has established itself as the standard for process modelling across industries – including in the enterprise environment of legal tech. Workflows create something that autonomous systems cannot deliver: transparency at system level.
07 Conclusion
The future of legal tech is orchestrated
Autonomous AI may be technically fascinating, but for legal processes it is structurally unsuitable. Legal tech does not get better because systems act on their own, but because they support people optimally in clearly orchestrated processes.
- Allow intelligence – Embed AI functions in processes in a targeted, rule-based way instead of letting them act autonomously and uncontrolled.
- Make responsibility visible – Complete logging, versioning and approval processes as the standard, not the exception.
- Keep decisions explainable – Deterministic procedures are the prerequisite for enterprise readiness.
- Design processes to be controllable – Explicitly modelled workflows remain maintainable, auditable and scalable in the long term.