Architecture Comparison
// WHY AGENTS COMPOUND HALLUCINATIONS
The autonomous-agent paradigm is the loudest current architecture in applied AI. It is also the wrong architecture for capital allocation.
Agents operate on probabilistic loops. A model produces an output; that output becomes the input to the next step; the loop continues until a stop condition. The architecture is built for open-ended exploration — research, code drafting, content generation — where iteration is the value.
Capital allocation is the opposite operating mode. It is a closed-ended constraint-verification problem. The deal team is not exploring; the deal team is asking whether a specific narrative survives contact with the underlying business physics. A compounded hallucination in that loop is a fiduciary liability, not an interesting next iteration.
The Doctrine
Agents are for autonomous tasks.
Compilers are for capital constraint verification.
§01
In a multi-step agent loop, the probability of a structurally correct answer is approximately the product of the per-step probabilities. Each step that introduces a hallucinated fact propagates it downstream. The loop has no built-in mechanism to halt and surface the error to the operator.
For a probabilistic generation task — "draft a marketing email" — a wrong intermediate is a tolerable noise floor. For a $5M capital allocation decision, the same noise floor produces a memo that reads correctly, cites correctly, and recommends correctly — based on a hallucinated revenue figure three steps back. The output is not auditable, because the trajectory was probabilistic.
§02
Agentic Loop
Probabilistic loop. A model output becomes the next-step input. The trajectory is non-deterministic.
Deterministic Compiler
Single deterministic pass. Constraints are verified in one reproducible compilation against the Judgment Graph.
Agentic Loop
Hallucinations compound. An incorrect intermediate finding propagates into every downstream step autonomously.
Deterministic Compiler
Errors are surfaced and halted. A failed constraint check returns a deterministic Kill Shot, not a downstream guess.
Agentic Loop
Open-ended task execution — research, code generation, content drafting where exploration is the point.
Deterministic Compiler
Closed-ended constraint verification — capital allocation, where the answer must be defensible to an LP or regulator.
Agentic Loop
Conversational log. Reproducibility is not guaranteed; the same input may produce a different trajectory.
Deterministic Compiler
Defensible Audit Log™. Every score is reconstructible from the citation-grade evidence.
§03
An LP review, a regulatory inquiry, or a board challenge all ask the same structural question: "What was the basis for this decision?" The acceptable answer is a reconstructible artifact, not a transcript of an autonomous loop.
Deterministic compilation produces a Defensible Audit Log™ by architectural property: same inputs, same findings, same evidence trail. Probabilistic agents cannot guarantee this. They are not designed to.
The architecture is the choice. We chose the one a fiduciary can defend.
We do not run agents.
We run a compiler.