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Apollo-1 introduces a fundamentally different approach. Rather than probabilistic LLM decisions or rigid step-by-step workflows, operators declare domain “truths” — statements about how the world works that the model uses to compute behavior from state.

Three Paradigms Compared

ApproachDecision-MakingGuarantee
LLM as BrainProbabilistic decision-makingCannot guarantee constraints
WorkflowsExplicit branch codingInflexible and difficult to maintain
Apollo-1 TruthsDeclarative statementsState computation with deterministic application

How Truths Work

Rather than step-by-step instructions, operators declare what must be true:
  • “Block payment processing without explicit user confirmation”
  • “Require ID verification for refunds over $200”
  • “Block ticket cancellation unless passenger is Business Class and Platinum Elite”
The model then:
1

Interpret

Interprets user input using neural language understanding
2

Compute

Computes explicit symbolic state from the conversation
3

Evaluate

Evaluates which truths apply to the current state
4

Execute

Executes actions deterministically based on the applicable truths

Key Benefits

  • Composability — Truths combine naturally without requiring explicit integration
  • Maintainability — Add, modify, or remove truths independently without cascading changes
  • Flexibility without lost control — Natural conversation with deterministic enforcement
  • Auditability — Every decision traces back to specific truths and state
  • Separation of concerns — Domain experts define truths; the model handles execution

Truth Conflict Resolution

When multiple truths apply to the same entity, Apollo-1 resolves conflicts through per-entity evaluation and priority by strictness:
  1. Block — highest priority
  2. Ask Question — medium priority
  3. Activate Tool — lowest priority
This ensures the most restrictive applicable truth always wins.