Why Defining Problem Boundaries Is Crucial for Effective AI Agents

We have always had the idea of letting AI automatically help us accomplish what we want—autonomous driving, automatic article writing, cooking, operating devices, and so on.

With AI development, this idea is getting closer to reality, but it has not yet been realized.

Large language models already possess strong knowledge and language expression abilities, capable of complex dialogue, code generation, logical reasoning, and even simulating a degree of "thinking". However, the gap between being talkative and actually completing tasks remains significant.

Consider another angle: when we ask a human to complete a task, we usually give a rough goal, then gradually clarify the problem scope, the steps, the available tools, and the criteria for judging success.

This process essentially defines the problem scope, and the degree of definition directly determines the difficulty of the task.

Example:

If you ask a person "help me check tomorrow's weather", the problem boundaries are clear: location, time, data source, and output format are relatively defined.

If you say "help me design a new product and propose a complete business strategy", the task boundaries are vague: who are the users? where is the target market? what is the budget? what are the success criteria? each dimension can spawn many sub‑questions.

The same reasoning applies to AI. Current LLMs and agent systems perform well on boundary‑clear problems such as Q&A, summarization, and code completion. But once the task boundary becomes fuzzy, dynamic, or dependent on external feedback, AI performance quickly drops.

We can view task difficulty as the degree to which AI needs to "clarify the problem boundary":

Boundary clear : inputs, outputs, and rules are explicit; AI can solve step‑by‑step like a fill‑in‑the‑blank task. This is currently AI's strength.

Boundary partially clear : some rules and goals exist, but AI must fill in assumptions, e.g., "write code for user login" requires choosing a framework, UI, etc.

Boundary highly uncertain : e.g., "help me plan a startup project" requires goal clarification, path selection, resource allocation, self‑evaluation, leading to confusion.

In other words, the more vague the problem boundary, the larger the range of possibilities AI must handle.

If not constrained, AI is like searching a completely unknown forest, not knowing exits or traps, leading to random wandering or circling and producing seemingly reasonable but unworkable solutions.

Humans facing complex or vague problems also first define the problem scope by asking:

What are the key variables?

What information is needed to make a decision?

Can we try a minimal version first to see if the direction is correct?

This way of thinking is a cognitive "scope compression" ability, aiming to compress an ill‑defined problem into an actionable range before expanding.

Compared with that, current LLMs and agent systems, despite strong generation and execution abilities, are still clumsy or unconscious in actively defining problem scope.

Common manifestations:

Lack of information priority judgment : when given a vague task, the model cannot decide which information must be clarified now versus later, and tends to fill everything at once.

No sense of a minimum viable path : the model prefers to generate a seemingly complete solution rather than first a minimal viable product (MVP) and then iterate.

Inability to recognize its own knowledge blind spots : the model does not know what it does not know, lacks meta‑cognitive responses, and continues generating plausible yet potentially contradictory content, which is risky in real tasks.

New agent architectures are trying to solve this problem. They emphasize:

Multi‑stage task decomposition : break a complex task into multiple stages, each with clear sub‑goals and expected outputs.

Reflection and self‑check mechanisms : after each step, the model self‑evaluates for reasonableness, omissions, or need to retry.

Information clarity assessment : the model identifies which information is insufficient for the next inference and proactively requests or hypothesizes it.

Dynamic path adjustment ability : when a path error is discovered, the model can abort the current chain, revert to a previous step, and replan instead of blindly proceeding.

These abilities form a "thinking loop" that gives the model a nascent capability to define problem boundaries.

In the real world, tasks are never straightforward or structurally clear:

Users may provide only a vague goal (e.g., "help me design a business model").

Information may be missing, interrupted, or feedback may change during execution.

Execution requires continual judgment of whether the direction remains correct.

To cope, AI needs not only information‑processing ability but also self‑regulation when information is insufficient.

This research has attracted broad academic attention. Some viewpoints include:

Sketch strategy : AI first generates multiple solution sketches, then decomposes them into sub‑tasks, executes, evaluates, and corrects, establishing several "versions of problem understanding" before converging.

Tree search + reward‑driven : AI explores multiple paths like climbing a mountain, evaluating each step's effect to decide whether to continue, helping efficiently define problem boundaries.

Only as assistant : AI serves as a thinking tool to generate alternatives, fill missing elements, and explain existing paths.

As AI users, what can we do?

Pose better questions : provide precise problem statements, define background, resources, constraints, and expected output format; this pre‑definition greatly improves AI output. Use progressive guidance: let AI complete a small sub‑task first, verify understanding, then expand.

Build a closed‑loop human‑AI collaboration process : users evaluate AI output promptly, give clear feedback, indicate correct directions, needed adjustments, and remaining issues. This feedback‑correction mechanism lets AI gradually adjust its understanding of problem boundaries. For complex tasks, adopt a "human‑in‑the‑loop" mode where AI generates options and details, and humans decide direction and quality.

Adapt to AI's cognitive limitations : recognize AI's weakness in abstract concepts, causal reasoning, and long‑term planning; break complex tasks into clear‑boundary sub‑problems, let AI operate within its strengths, and keep AI as an assistive tool rather than a decision‑maker for high‑risk or innovative tasks.

For AI programming, a good practice is:

Experience first (including personal experience or industry best practices), pre‑build the overall architecture for AI, and decompose complex tasks into a series of boundary‑clear, cognitively manageable sub‑tasks. Each sub‑task should lie within the model's capability, be accurately understood and executed, and steadily advance the overall goal, avoiding recursive trial‑and‑error and path deviation.

That is all.