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The Core Idea

Hive is outcome-driven: you define what success looks like, and the agent adapts toward that result. Instead of hardcoding every step, you define:
  • A goal
  • Success criteria (weighted quality checks)
  • Constraints (hard and soft boundaries)
  • Context (domain knowledge and preferences)

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Task-Driven vs Goal-Driven vs Outcome-Driven

ParadigmMain QuestionLimitation
Task-Driven”Did each step run?”Correct steps can still produce poor results
Goal-Driven”Are we aiming correctly?”Goals can be vague without measurable criteria
Outcome-Driven”Did we produce the desired result?”Requires clear metrics and evaluation discipline

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Goal as a Structured Contract

In Hive, a goal is a structured object with three parts.

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Success Criteria

Each criterion defines a measurable quality signal and a weight. Common metrics:
  • output_contains
  • output_equals
  • llm_judge
  • custom
Weights allow trade-offs across multiple quality dimensions.

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Constraints

Constraints define non-negotiables and preferences.
  • Hard constraints: violation triggers failure/escalation
  • Soft constraints: violation is tolerated with warning/replan
Typical categories:
  • time
  • cost
  • safety
  • scope
  • quality

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Context

Context supplies durable background information that should influence agent decisions and LLM behavior.

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Why This Model Matters

  1. The agent can self-correct during execution against explicit criteria.
  2. Evolution has a target because failures map to specific unmet criteria.
  3. Humans retain control through hard constraints and intervention gates.

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Goal Lifecycle

Goals typically move through: DRAFT -> READY -> ACTIVE -> COMPLETED | FAILED | SUSPENDED