Divide processes into deliberative & impromptu reasoning

An eight-factor AI application explicitly distinguishes between different types of reasoning processes. The primary distinction is between deliberative reasoning (systematic, multi-step thinking) and impromptu reasoning (quick, context-based responses). Each type should be clearly identified and handled appropriately.

Reasoning types in an eight-factor app are explicitly defined:

  • If all queries are handled the same way, it’s not an eight-factor app – it wastes resources on simple tasks or underperforms on complex ones
  • If reasoning patterns aren’t explicit, the application can’t optimize for different types of tasks

The two primary reasoning modes have distinct characteristics:

Deliberative Reasoning:

  • Multiple steps of thought
  • Explicit intermediate results
  • Verifiable logic chain
  • Resource intensive
  • Used for complex tasks

Impromptu Reasoning:

  • Single-step responses
  • Direct context to output
  • Quick turnaround
  • Resource efficient
  • Used for simple tasks

Bad practice - one-size-fits-all reasoning:

def handle_request(request):
    prompt = f"Please help with this request: {request}"
    return model.generate(prompt)
    # All requests treated the same way

Good practice - differentiated reasoning paths:

class ReasoningRouter:
    def route_request(self, request: Request) -> Response:
        complexity = self.assess_complexity(request)
        
        if complexity.is_simple():
            return self.impromptu_handler.respond(request)
        
        return self.deliberative_handler.process(request)

class DeliberativeHandler:
    def process(self, request: Request) -> Response:
        plan = self.create_reasoning_plan(request)
        context = self.initial_context(request)
        
        for step in plan.steps:
            result = self.execute_step(step, context)
            context.update(result)
            
            if self.should_revise_plan(result):
                plan = self.revise_plan(plan, context)
        
        return self.synthesize_response(context)

class ImpromptuHandler:
    def respond(self, request: Request) -> Response:
        template = self.select_response_template(request)
        return self.generate_direct_response(template, request)

Complexity assessment should be explicit:

class ComplexityAssessor:
    def assess(self, request: Request) -> Complexity:
        factors = {
            "multi_step": self.requires_multiple_steps(request),
            "tool_use": self.requires_tools(request),
            "context_depth": self.context_complexity(request),
            "verification": self.needs_verification(request)
        }
        
        return Complexity.from_factors(factors)

This approach enables:

  • Efficient resource utilization
  • Appropriate handling of different task types
  • Clear reasoning paths
  • Verifiable decision processes
  • Optimized response times

Reasoning modes should be explicitly tracked:

  • Mode selection should be logged
  • Reasoning steps should be traced
  • Outcomes should be measured
  • Performance should be monitored
  • Resources should be allocated appropriately

Applications may implement hybrid reasoning:

class HybridReasoner:
    def process(self, request: Request) -> Response:
        # Start with quick assessment
        quick_response = self.impromptu_handler.assess(request)
        
        if quick_response.confidence >= self.threshold:
            return quick_response
            
        # Fall back to deliberative if needed
        return self.deliberative_handler.process(request)

This pattern enables:

  • Optimal resource allocation
  • Appropriate depth of reasoning
  • Clear processing paths
  • Measurable outcomes
  • Adaptive response strategies

Reasoning implementations should optimize for:

  • Appropriateness - matching reasoning to task
  • Efficiency - using resources wisely
  • Clarity - explicit reasoning paths
  • Verifiability - traceable decisions
  • Adaptability - adjusting to task needs

Each reasoning mode should have:

  • Clear entry criteria
  • Defined success metrics
  • Resource budgets
  • Error handling strategies
  • Performance benchmarks