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[dev_260101_06] Level 5 Component Selection Decisions

Date: 2026-01-01 Type: Development Status: Resolved Related Dev: dev_260101_05

Problem Description

Applied Level 5 Component Selection parameters from AI Agent System Design Framework to select LLM model, tool suite, memory architecture, and guardrails for GAIA benchmark agent MVP.

Key Decisions

Parameter 1: LLM Model → Claude Sonnet 4.5 (primary) + Free API baseline options

  • Primary choice: Claude Sonnet 4.5
    • Reasoning: Framework best practice - "Start with most capable model to baseline performance, then optimize downward for cost"
    • Capability match: Sonnet 4.5 provides strong reasoning + tool use capabilities required for GAIA
    • Budget alignment: Learning project allows premium model for baseline measurement
  • Free API baseline alternatives:
    • Google Gemini 2.0 Flash (via AI Studio free tier)
      • Function calling support, multi-modal, good reasoning
      • Free quota: 1500 requests/day, suitable for GAIA evaluation
    • Qwen 2.5 72B (via HuggingFace Inference API)
      • Open source, function calling via HF API
      • Free tier available, strong reasoning performance
    • Meta Llama 3.3 70B (via HuggingFace Inference API)
      • Open source, good tool use capability
      • Free tier for experimentation
  • Optimization path: Start with free baseline (Gemini Flash), compare with Claude if budget allows
  • Implication: Dual-track approach - free API for experimentation, premium model for performance ceiling

Parameter 2: Tool Suite → Web browser / Python interpreter / File reader / Multi-modal processor

  • Evidence-based selection: GAIA requirements breakdown:
    • Web browsing: 76% of questions
    • Code execution: 33% of questions
    • File reading: 28% of questions (diverse formats)
    • Multi-modal (vision): 30% of questions
  • Specific tools:
    • Web search: Exa or Tavily API
    • Code execution: Python interpreter (sandboxed)
    • File reader: Multi-format parser (PDF, CSV, Excel, images)
    • Vision: Multi-modal LLM capability for image analysis
  • Coverage: 4 core tools address primary GAIA capability requirements for MVP

Parameter 3: Memory Architecture → Short-term context only

  • Reasoning: GAIA questions are independent and stateless (Level 1 decision)
  • Evidence: Zero-shot evaluation requires each question answered in isolation
  • Implication: No vector stores/RAG/semantic memory/episodic memory needed
  • Memory scope: Only maintain context within single question execution
  • Alignment: Matches Level 1 stateless design, prevents cross-question contamination

Parameter 4: Guardrails → Output validation + Tool restrictions

  • Output validation: Enforce factoid answer format (numbers/few words/comma-separated lists)
  • Tool restrictions: Execution timeouts (prevent infinite loops), resource limits
  • Minimal constraints: No heavy content filtering for MVP (learning context)
  • Safety focus: Format compliance and execution safety, not content policy enforcement

Parameter 5: Answer Synthesis → LLM-generated (Stage 3 implementation)

  • Reasoning: GAIA requires extracting factoid answers from multi-source evidence
  • Evidence: Answers must synthesize information from web searches, code outputs, file contents
  • Implication: LLM must reason about evidence and generate final answer (not template-based)
  • Stage alignment: Core logic implementation in Stage 3 (beyond MVP tool integration)
  • Capability requirement: LLM must distill complex evidence into concise factoid format

Parameter 6: Conflict Resolution → LLM-based reasoning (Stage 3 implementation)

  • Reasoning: Multi-source evidence may contain conflicting information requiring judgment
  • Example scenarios: Conflicting search results, outdated vs current information, contradictory sources
  • Implication: LLM must evaluate source credibility and recency to resolve conflicts
  • Stage alignment: Decision logic in Stage 3 (not needed for Stage 2 tool integration)
  • Alternative rejected: Latest wins / Source priority too simplistic for GAIA evidence evaluation

Rejected alternatives:

  • Vector stores/RAG: Unnecessary for stateless question-answering
  • Semantic/episodic memory: Violates GAIA zero-shot evaluation requirements
  • Heavy prompt constraints: Over-engineering for learning/benchmark context
  • Procedural caches: No repeated procedures to cache in stateless design

Future optimization:

  • Model selection: A/B test free baselines (Gemini Flash, Qwen, Llama) vs premium (Claude, GPT-4)
  • Tool expansion: Add specialized tools based on failure analysis
  • Memory: Consider episodic memory for self-improvement experiments (non-benchmark mode)

Outcome

Selected component stack optimized for GAIA MVP: Claude Sonnet 4.5 for reasoning, 4 core tools (web/code/file/vision) for capability coverage, short-term context for stateless execution, minimal guardrails for format validation and safety.

Deliverables:

  • dev/dev_260101_06_level5_component_selection.md - Level 5 component selection decisions

Component Specifications:

  • LLM: Claude Sonnet 4.5 (primary) with free baseline alternatives (Gemini 2.0 Flash, Qwen 2.5 72B, Llama 3.3 70B)
  • Tools: Web (Exa/Tavily) + Python interpreter + File reader + Vision
  • Memory: Short-term context only (stateless)
  • Guardrails: Output format validation + execution timeouts

Learnings and Insights

Pattern discovered: Component selection driven by evidence-based requirements (GAIA capability analysis: 76% web, 33% code, 28% file, 30% multi-modal) rather than speculative "might need this" additions.

Best practice application: "Start with most capable model to baseline performance" prevents premature optimization. Measure first, optimize second.

Memory architecture principle: Stateless design enforced by benchmark requirements creates clean separation - no cross-question context leakage.

Critical connection: Tool suite selection directly impacts Level 6 framework choice (framework must support function calling for tool integration).

Changelog

What was changed:

  • Created dev/dev_260101_06_level5_component_selection.md - Level 5 component selection decisions
  • Referenced AI Agent System Design Framework (2026-01-01).pdf Level 5 parameters
  • Referenced GAIA_TuyenPham_Analysis.pdf capability requirements (76% web, 33% code, 28% file, 30% multi-modal)
  • Established Claude Sonnet 4.5 as primary LLM with free baseline alternatives (Gemini 2.0 Flash, Qwen 2.5 72B, Llama 3.3 70B)
  • Added dual-track optimization path: free API for experimentation, premium model for performance ceiling