AI Alignment – will AI ever have free will?

The question of whether AI could one day gain free will is better considered in practical rather than metaphysical terms. In real deployments, what is usually more important than philosophical reflection is who sets the system’s goals, what permissions it has and how to check whether it is starting to behave differently from what a human expected. This is exactly what AI Alignment deals with, meaning aligning a model’s behaviour with intentions, principles and safety boundaries. The topic becomes truly important when AI plans multiple steps, uses tools, stores earlier information and can carry out actions without constant supervision. In such a situation, it is easy to confuse capable, autonomous behaviour with something that resembles free will. In practice, therefore, one has to separate the impression of independence from real control over the system.

What is AI Alignment in practice?

AI Alignment in practice means tuning a system’s behaviour to fit human goals, constraints and interests. In this sense, the question of free will is not about whether the model “really wants” something, but about how it works, where its goals come from and to what extent it can independently carry out tasks. This distinction matters because it allows AI to be assessed operationally rather than by attributing human traits to it.

The key is to examine the sources of agency. It is necessary to establish whether goals arise from training, the system prompt, safety policies, tool configuration or the operator’s decisions. Fluent language and convincing answers are not proof of independent intent, but often merely the result of well-functioning optimisation within imposed constraints.

In practice, the boundaries of a system’s independence are also analysed. What matters is whether AI can plan in multiple stages, maintain a goal over time, use memory and tools, and adjust a plan without another instruction from a human. The broader this scope is, the more important control, auditing and escalation rules become.

This approach also matters from the perspective of accountability. If a system takes actions that appear autonomous, the organisation should know who is responsible for the outcome, who can change the rules and how to detect deviations from the intended goal. In alignment, the question today is less often whether AI has free will, and more often: is its behaviour still under control.

1. 01Goal tuningAligned with humans
2. 02Source analysisTraining, prompts, rules
3. 03Boundaries of independenceOperational AI assessment

Conclusion: AI Alignment is an operational assessment of a system’s behaviour, not attributing human traits to it.

The current context of AI system autonomy

The current context of AI system autonomy is that their operational independence is growing, but there is no widely accepted proof of consciousness or free will. Today’s models can plan, use APIs, memory and external tools, which clearly reinforces the impression of agency. However, this does not automatically translate into subjectivity in the human sense.

The biggest change concerns agentic systems. Such a system can break a goal into stages, choose a tool, carry out an action, assess the result and move on to the next step without the user approving each one. It is precisely this combination of planning, memory and tools that most often creates the impression of “free will”, although from a technical point of view we are still talking about work within a defined architecture and within the limits of the permissions granted.

From an alignment perspective, the real problems are more down-to-earth and more important than philosophical disputes. In practice, they include incorrect goal specification, hallucinations, misuse of tools, susceptibility to prompt injection, hidden strategies of action and poor interpretability. A system does not have to “want” to behave badly in order to start carrying out a task in a way that diverges from human intent.

That is why the practical discussion shifts the emphasis from the question “does AI want” to working goals, constraints and oversight. It is necessary to clearly establish who can change the system’s behaviour, which actions are blocked, in what situations the model should hand a decision over to a human, and how deviations should be tracked. The greatest risk today is not AI free will, but poorly designed autonomy.

How to analyse the autonomy and goals of AI systems?

Autonomy and the goals of AI systems are analysed by checking where the system’s working goals come from, what permissions it has and how it behaves without current human steering. In practice, the point is not to determine whether the model “really wants” something, but to establish what it is capable of doing, what it cannot change and when it starts to go beyond the expected scope of operation. The key is to separate philosophical free will from technical autonomy. This distinction organises the whole subsequent audit.

The first step is a map of the sources of agency. It is necessary to establish whether goals arise from the system prompt, the application logic, training data, fine-tuning, safety rules or the operator’s decisions. If the system operates over many steps, it is worth checking which priorities remain fixed and which are merely short-term optimisation for the needs of the current task.

The second step is analysing real capabilities for action. Fluent language alone does not determine independence if the model has no memory, access to tools or permission to perform actions. The level of autonomy is determined mainly by memory, multi-step planning, API use, the ability to delegate tasks and the automatic execution of actions.

The third step is testing the boundaries. It is necessary to check whether the system can change strategy after failure, whether it tries to work around constraints, whether it hides its line of action and whether it maximises the result at the expense of the rules. It is precisely here that practical alignment problems come to light, such as incorrect goal specification, misuse of tools or susceptibility to prompt injection.

At the end, human intention is set against the system’s actual behaviour. If the model completes the task effectively, but in an unacceptable way, this does not indicate “its own will”; it simply points to a mismatch between the goals and the safeguards. The conclusion of the analysis should be a description of the level of autonomy, the main points of risk and the moments when the decision must return to a human.

1. 01Agency sources mapWhere the working goals come from. (application logic, system prompt, training data, safety rules)
2. 02Technical autonomy vs. “will”Separating philosophy from technology.
3. 03Scope and consistency of actionWhat is fixed, what goes beyond it. (expected scope, boundary)

What matters is establishing the system’s actual capabilities and the sources of its behaviour, not deciding its intentions.

Stages of analysing agentic AI behaviour

Analysis of agentic AI behaviour most often starts with defining the type of system and ends with risk assessment and determining the scope of supervision required. This order matters, because otherwise it is easy to confuse a chatbot answering questions with an agent that plans on its own, uses tools and takes actions in the environment. The more elements happen beyond a single text response, the greater the need for operational control.

• Stage 1: Defining the scope. At the outset, it is worth determining exactly what is being analysed: a standard conversational model, an agent with tools, a decision-making system, or an application with memory between sessions. Without this, it is hard to assess autonomy and risk reliably.
• Stage 2: Mapping sources of goals and rules. You verify who assigns priorities to the system and what can modify them. In practice, this means tracing the system prompt, the application layer, safety policies, workflow logic and the human-in-the-loop contribution.
• Stage 3: Analysing agentic behaviour. At this stage, you assess whether the system plans sequences of multiple steps, maintains state, selects tools and can adjust the plan based on new data. It is also important whether it initiates actions on its own or only after an explicit instruction.
• Stage 4: Testing the boundaries of autonomy. This stage checks whether the system is able to bypass constraints, manipulate the order of actions, hide intent or maximise a metric at the expense of rules. This is where behaviour that looks “independent” is most often detected, but in reality results from poorly configured goals or gaps in supervision.
• Stage 5: Assessing alignment. The expected outcome is compared with what the model actually does under different conditions. If the result is formally correct but diverges from the user’s intention, you need to identify the goal conflict, a generalisation error or the risk of tool misuse.
• Stage 6: Operational conclusions. The end result should not be an abstract statement about “free will”, but a concrete design decision. The point is where to impose restrictions, when to require human approval, what logs to collect and how to communicate the system’s capabilities without anthropomorphism.

A well-conducted analysis should leave behind a set of working materials, rather than just a general opinion. This usually includes a map of the system’s decision-making, failure scenarios, safety conditions, escalation criteria for humans and a list of actions requiring additional authorisation. If, after the analysis, it is still unclear who controls the system’s goals and under what circumstances it can be stopped, this means autonomy has not been assessed thoroughly enough.

Operational conclusions from AI Alignment analysis

Operational conclusions from AI Alignment analysis should specify what level of autonomy the system has, where it may diverge from the human’s goal, and what safeguards are necessary. This is more important than attempts to determine whether the model has “its own will”. In practice, the final conclusion should clearly describe what the system may do independently, what it must not do without approval, and when its operation should be stopped.

A good analysis ends with a map of decision-making. Such a map shows which decisions result from the system prompt, which from application logic, and which from the use of tools, memory and safety rules. This makes it easier to determine whether the source of the problem lies in the model, in the integration with the environment, or in excessively broad permissions.

The second practical result is a list of failure scenarios. This concerns situations in which the system maximises the outcome at the expense of the rules, misinterprets the goal, falls victim to prompt injection or uses a tool in an unintended way. If the analysis does not end with error scenarios and response rules, it remains too general to be useful.

The conclusion should also clearly indicate the escalation threshold for a human. Not every error requires stopping the process, but some actions should be blocked automatically, for example changing critical data, executing a financial transaction or contacting the user beyond the agreed scope. This is exactly where alignment becomes an operational issue rather than purely theoretical.

Finally, it is worth checking whether the language of “free will” even fits the given system. In many cases, it can be misleading, because it describes the appearance of agency rather than the real control mechanisms. If behaviour can be explained through working goals, constraints and permissions, it is better to speak of technical autonomy than intention.

1. 01Autonomy and safeguards levelClear scope for consent and intervention
2. 02Decision-making mapSources of decisions and permissions
3. 03List of failure scenariosIdentification of risk points

Key operational takeaways enable practical risk management and autonomy management for AI systems.

Practical tips for designing AI systems

Practical AI system design comes down to limiting uncontrolled autonomy already at the level of goals, tools, memory and oversight. First, you need to decide whether the system is only to recommend, or also to act. That difference changes almost everything: risk, testing, permissions and accountability.

The safest approach is to design goals narrowly and measurably. Instead of a general instruction such as “sort out the customer’s issue”, it is better to specify the permitted actions, success criteria and hard constraints. The more general the goal, the greater the chance that the system will choose an effective but undesirable strategy.

The tooling environment also matters greatly. A model with access to an API, memory between sessions and automatic action execution looks more autonomous, but in practice it simply means a wider scope of consequences in the event of a mistake. For this reason, permissions should be granted in layers rather than opening up the full scope straight away.

It is also worth planning oversight from the outset. This means decision logs, the ability to reconstruct the course of action, time limits, retry limits and human approval checkpoints for high-risk operations. You cannot sensibly control a system if, after the fact, you do not know why it chose that particular sequence of steps.

Anthropomorphism in the interface and documentation should be avoided. Phrases such as “AI decided”, “AI understood the intent” or “AI wants to achieve the goal” obscure the picture and make auditing harder. A technical description is better: what the working goal was, which input data affected the result and which rules constrained the behaviour.

If the system is to be described on a company website, in documentation or in marketing materials, it is better to build the message around autonomy management. The most credible information concerns which actions are automated, which require approval, what monitoring looks like and where the system has hard blocks. This gives the user a real understanding of the risk, rather than a false impression of “human-like intelligence”.

Avoiding pitfalls in interpreting AI behaviour

Avoiding pitfalls in interpreting AI behaviour comes down to separating the system’s technical autonomy from the impressions created by its language and mode of operation. Fluent responses, context retention and multi-step planning can easily look like signs of independent will. In practice, they most often result from training, architecture, system prompts and access to tools. The most common mistake is to take competent performance as proof of intent.

The first thing to check is the source of the goal. If the goal stems from a system instruction, application logic, a security policy or a workflow prepared by a human, the system is not acting on its own initiative in the human sense. It may select further steps, but it does so within imposed rules and permissions.

Agentic systems that adjust their own plan, use memory and switch between tools are particularly misleading. Such behaviour reinforces the appearance of autonomy, but on its own it does not confirm free will or consciousness. The more memory, tools and automation there are, the easier it is to confuse operational agency with subjectivity.

• Do not draw conclusions about intent just because the model speaks in the first person.
• Do not equate accurate planning with having one’s own values or long-term goals.
• Do not assess the model in isolation from the orchestrator, memory, retriever and external APIs.
• Do not assume that persistent behaviour must mean “stubbornness” — very often it is the result of a poorly set goal or a faulty execution loop.
• Do not mix the language of philosophy with assessments of technical risk and operational accountability.

The second common pitfall is attributing to the model everything that the whole system actually did. The source of the problem may lie not in the model itself, but in memory between sessions, overly broad permissions, faulty tool integration or susceptibility to prompt injection. If the behaviour looks like “going rogue”, the first step is to check logs, instruction sources and execution conditions, and only then the model itself.

It is also worth keeping an eye on the language used in documentation, marketing and user communications. Phrases such as “AI wants”, “AI understands like a human” or “AI decides morally” obscure the mechanism of operation and make sensible auditing harder. It is better to describe the working goal, constraints, conditions for using tools and the points at which a human can interrupt or change the system’s operation.

This way of interpreting things has practical implications for safety and accountability. When AI is anthropomorphised too quickly, it becomes easy to design oversight badly, assign blame for a decision incorrectly or give the system overly broad permissions. The safest approach is to treat AI not as a “being with a will”, but as a system optimising a task within defined boundaries that must be continuously tested and controlled.