An agent can work very hard inside the wrong idea.

That is the failure I keep thinking about. It finds an explanation, builds a plan around it, and then spends the rest of its effort making that plan more elaborate. Each step sounds reasonable. The whole thing goes nowhere.

We usually ask for more persistence. Try again. Think harder. Use another tool. But persistence is only useful when the system can also reconsider what it is being persistent about.

I wonder whether we are designing too much certainty into our agents, too early.

The comfort of one explanation

Imagine an agent trying to publish a page. The first attempt fails, and it decides that access is the problem. It searches for a login, checks permissions, opens another browser and proposes a new authentication route. An hour later, it is still solving access.

But suppose the failure happened before the request left the machine. A broken local command would explain everything. The account was never the problem.

This is a hypothetical example, but the shape is familiar: a plausible explanation becomes the centre of gravity. Further reasoning gets pulled towards it. More activity can make the original mistake harder to see because there is now so much work invested in it.

The useful interruption is small: what observation would distinguish a local failure from a remote rejection? Read the actual failure. Find where execution stopped. Let the evidence choose the next branch.

What entropy has to do with it

There is a precise version of this tension in reinforcement learning. In maximum-entropy methods such as Soft Actor-Critic, training balances expected reward with policy entropy. Keeping some spread in the possible actions encourages exploration and can help avoid settling prematurely on a poor solution. The original Soft Actor-Critic paper develops that approach.

That gives me a useful analogy for agent design. A system needs room to consider another route before its first interpretation hardens into certainty.

The analogy has limits. Training a policy and running a language-model agent are different processes. Raising a model’s sampling temperature changes the distribution of its output; it does not automatically give the agent better judgement, better evidence or a genuinely different hypothesis. A more surprising sentence can still carry the same mistaken assumption.

So when I talk about entropy here, I mean deliberately preserving alternatives in the workflow. It is a design proposal, not a claim that a single mathematical knob cures tunnel vision.

Different questions create different paths

Consider three ways to examine the same failure. One asks whether the plan is wrong. Another asks whether the environment changed. A third asks whether the result already happened and the confirmation was lost.

Those are meaningfully different investigations. Three agents repeating “try to fix it” may simply produce three versions of the same answer.

I would start with a short list of competing explanations and the cheapest observation that could eliminate each one. The point is to spend a little attention on breadth before spending a great deal on depth. Sometimes one log line settles it. Sometimes a fresh reader needs to see the evidence without inheriting the first agent’s confident summary.

A useful system should be able to abandon its explanation without abandoning the work.

Memory matters here too. A remembered workaround can be valuable, but it should arrive with the conditions under which it worked. Otherwise yesterday’s successful route becomes today’s tunnel.

Exploration needs a boundary

I do not want an agent improvising randomly while moving money, changing records or sending messages. Those actions need a stable understanding of what is authorised and what already happened. Uncertainty about a receipt is a reason to inspect the result before trying again.

The room for variation belongs primarily in investigation: different searches, competing diagnoses, reversible experiments, alternative drafts. Once the evidence supports an action, execution should become narrow and deliberate.

There is also a cost to keeping every possibility alive. An agent that endlessly explores is another kind of failure. I would give exploration a budget and a stopping rule: investigate until the evidence distinguishes the plausible explanations, then commit to the best-supported path. Reopen the question when a new observation contradicts it.

A way to test the idea

The test I would run is simple to describe. Give two systems the same tasks with a misleading first clue. Let one continue normally. Give the other a bounded checkpoint that asks for competing explanations and evidence against its current plan.

Then compare completed outcomes, time, cost and unnecessary actions. Count whether the system escaped the wrong explanation. Also count the easy tasks it made slower. A beautiful theory of exploration is worth little if it adds ceremony everywhere and helps nowhere.

What interests me is the possibility of an agent that is both determined and intellectually mobile. It can hold the goal firmly while holding its explanation lightly.

Perhaps that is the kind of order intelligent systems need: enough structure to finish the work, and enough openness to discover that the work requires a different path.