Agent Goal Generation
Implementing goal generation for AI agents — from subgoal decomposition and intrinsic motivation through goal discovery, prioritization, and dynamic replanning.
When to Use
- Agents that need to generate their own goals autonomously
- Hierarchical agents that decompose top-level goals into subgoals
- Implementing curiosity-driven or intrinsic motivation
- Building agents that can reprioritize goals dynamically
Goal Generation Methods
GOAL_GENERATION = {
'task_decomposition': 'LLM-based or symbolic decomposition of high-level goal into subgoals',
'intrinsic_motivation': 'Curiosity (novelty), competence (mastery), autonomy (choice)',
'goal_discovery': 'Agent discovers goals from environment patterns or user behavior',
'multi_agent_subgoal': 'Agents propose subgoals to each other, negotiate priority',
}
class GoalGenerator:
"""Generate and manage agent goals."""
def __init__(self):
self.goals = []
self.completed = []
self.priorities = {}
def decompose_task(self, top_level: str, context: str) -> List[Dict]:
"""Decompose a top-level goal into subgoals."""
subgoals = [
{'description': f'Analyze {top_level}', 'status': 'pending', 'dependencies': []},
{'description': f'Plan approach for {top_level}', 'status': 'pending', 'dependencies': [0]},
{'description': f'Execute {top_level}', 'status': 'pending', 'dependencies': [1]},
{'description': f'Verify {top_level} completion', 'status': 'pending', 'dependencies': [2]},
]
for sg in subgoals:
self.goals.append(sg)
return subgoals
def next_goal(self) -> Dict:
for goal in self.goals:
if goal['status'] == 'pending':
deps = all(self.goals[d]['status'] == 'completed' for d in goal.get('dependencies', []))
if deps: return goal
return None
Verification Checklist
- Goal decomposition produces meaningful subgoals
- Subgoal dependencies correctly tracked
- Intrinsic motivation (curiosity) balanced with extrinsic goals
- Dynamic reprioritization when environment changes
- Goal completion verified (not just assumed)
- Unreachable goals detected and re-assessed