Idea: Multi-Stage Agent Architecture for Improved Decision Accuracy

[Aladincz]

Idea: Multi-Stage Agent Architecture for Improved Decision Accuracy

Note: This proposal was drafted with AI assistance to help clearly articulate my thoughts. Some technical details may require refinement by human reviewers as my expertise is not big. Concept probably mimic techniques used on other places.

Summary

Propose implementing a 2-3 stage agent pipeline where a single model operates as multiple specialized agents with distinct perspectives, evaluating decisions sequentially before producing final output. This aims to improve accuracy beyond single-pass inference while maintaining cost efficiency compared to routing all queries to larger models.

Background

Current single-pass inference models achieve approximately 70% accuracy on certain tasks. Alternative approaches using larger models incur higher costs and latency. This proposal explores whether structured multi-stage processing with the same model can achieve meaningful accuracy gains at lower marginal cost.

Proposed Architecture

Three stages, same base model, different prompts:

1. Analyzer: Extract facts, identify key elements, produce initial assessment
2. Critic: Evaluate weaknesses, check logical gaps, flag uncertainties
3. Arbitrator: Synthesize outputs, resolve conflicts, make final determination

Key addition: Later stages can reject earlier work and trigger fallback to larger model.

Expected Benefits

Metric	Single-Pass	Multi-Stage	Notes
Accuracy	~70%	72-80% (estimated)	Varies by task complexity
Latency	1x	2-3x	Proportional to stages
Cost	1x	2-3x	Per-query inference cost

Risk Analysis

1. Error Propagation (Critical)

Issue: If Stage 1 makes a fundamental error, Stage 2/3 may refine rather than correct it.

Mitigation:

• Stage 2 must be designed to explicitly challenge Stage 1, not just validate
• Implement confidence scoring at each stage
• Fallback trigger: If overall rating drops below threshold, route to larger/advanced model (e.g., advanced tier) or external evaluation

2. Selective Deployment

Instead of applying this to all queries:

Domain	Priority	Rationale
Complex reasoning	High	Known weak area
Multi-hop queries	High	Fact aggregation prone to errors
Ambiguous requests	Medium	Multiple perspectives help
Straightforward QA	Low	Single-pass sufficient

This reduces overhead and focuses resources where improvement is most needed.

3. No Improvement Scenario

Issue: Architecture adds overhead without measurable accuracy gain.

Mitigation:

• Opt-in feature for identified weak domains only
• A/B testing framework if infrastructure available
• Automatic rollback if metrics degrade

Limitations & Unknowns

Since benchmarking infrastructure is not currently available, this would begin as a theoretical proof-of-concept. Empirical validation requires:

• Query classification to detect weak-performance domains
• Confidence thresholds for fallback triggers
• Latency tolerance measurements from users

Open Questions

• What accuracy/confidence threshold triggers fallback to advanced model?
• Should thresholds adapt by query type?
• Is sequential (2→3→output) better than parallel critics?
• How much latency increase is acceptable before quality perception drops?

Conclusion

Multi-stage agent architecture trades latency/cost for accuracy. While improvement is not guaranteed and risks exist (especially error propagation), selective deployment in weak-performance domains with fallback mechanisms could provide net benefit.

Recommend starting with 2-stage pilot on specific query categories before expanding scope.

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Related Issues: #XXX (model performance benchmarks), #YYY (fallback routing)
Labels: architecture, experimentation, risk-assessment