If you are building agentic workflows for consequential decisions—things like financial movements, infrastructure deployments, or tech stack adoption—you have to stop treating LLM outputs as conclusions. Most of what we see from models is just fluent pattern completion disguised as analysis. When an agent says, "There is a 70% chance this works," it isn't doing math; it's just mimicking the authoritative tone it was rewarded for during RLHF. To build maintainable, reliable systems, you need to move from narrative-driven outputs to falsification-driven ones.

I've implemented a strict structural constraint in my own pipelines: no claim is accepted unless it includes an explicit falsification condition. A claim without a kill condition isn't a prediction; it's just a story. If you can't define the exact observable event that would prove the claim wrong, you shouldn't be using that output to drive a decision.

I use a set of reusable decision units to enforce this across different domains. The most important rule is banning the LLM from generating its own probabilities. If you need a number, use a statistical model and validate it with a Brier score. Let the LLM interpret the data, not invent the likelihood.

For any technical or strategic thesis, I require a falsifier block. This forces the model to define the boundaries of its own logic.

falsifier:
 condition: [observable event that invalidates the thesis]
 threshold: [measurable value, not vague]
 evaluation_date: [concrete date to check]
Example: technology adoption decision

falsifier:
 condition: >
 Library has no releases for 6 months, OR
 Critical CVE published with no patch in 30 days, OR
 Primary maintainer announces deprecation
 threshold: any of the above triggers

This approach changes the output from a vague assertion to a testable hypothesis. You also have to enforce strict labeling for observations. If an agent sees two things happening at once, it cannot label it causal. It must categorize the data:

FACT: Directly observed with a source.

INFERENCE: Derived from facts with stated assumptions.

UNIDENTIFIED: Correlation only, mechanism unknown.

SPURIOUS: Co-occurrence with no causal link.

By forcing this structure, you ensure that your decision-making logic is grounded in reality rather than just the most probable next token.