Multi-AI Collaboration: Claude + Gemini + Codex

By Waterson AI Team · April 2026 · 5 min read · Part of the Three-Layer Architecture series

Don't let Claude do everything alone. Once your three-layer skill architecture is in place, you can distribute work across multiple AI providers — maximizing throughput while minimizing cost.

The Delegation Hierarchy

Each AI has a different strength and a different price point. Use the right model for the right task:

AI	Role	When to use	Cost
Claude Opus	Orchestrator	Complex decisions, quality control, final integration	Highest — reserve for core work
Claude Sonnet	Workers	Writing, reviewing, code generation, auditing	Medium — your main workforce
Gemini Flash/Pro	Researchers	Google Search grounding, fact verification, SEO analysis	Free (1,000 req/day) — use aggressively
Codex (GPT)	Code reviewer	HTML/CSS/JS quality, accessibility audit, competitive analysis	Subscription — use until quota exhausted
Claude Haiku	Lightweight tasks	Memory filtering, simple formatting, quick lookups	Cheapest — high-volume low-complexity

Fallback Chains

Skills can define tool fallback chains, making them resilient to quota limits and API outages:

## Research Step
1. Try: gemini -m gemini-2.5-flash -p "{{query}}" --output-format text
2. Fallback: Claude Sonnet sub-agent with WebSearch tool
3. Fallback: Manual research prompt to user

Because this fallback logic lives in the skill file rather than in memory, it doesn't cost tokens during conversations where research isn't needed.

Configure in CLAUDE.md

## Multi-AI Collaboration

Gemini CLI: `echo "Y" | gemini -m gemini-2.5-flash -p "QUERY" --output-format text`
Codex CLI:  `codex exec --full-auto -C /path "TASK"`

Fallback chain:
1. Gemini Flash (free) → 2. Codex (subscription) → 3. Claude Sonnet (paid per token)

Always try free/cheaper options first for:
- Web research, fact checking
- Code review, linting
- SEO analysis, proofreading
- Bulk formatting, translation

Key principle: Burn the free tokens first. Gemini gives you 1,000 requests/day for free. Codex subscriptions include a token budget. Use them aggressively before falling back to Claude.

Real Example: 42 Agents, One Session

agents dispatched in a single working session

Here's how the agents were distributed:

25 Claude Sonnet agents — writing, reviewing, fixing
10 Gemini Flash tasks — citation verification, SEO checks, proofreading
4 Codex tasks — code review, accessibility audit
2 Gemini Pro tasks — deep content analysis
1 Claude Opus orchestrator — planning, integration, quality control

Output: one working day

5 AIA CEU courses (284 slides total)
7 blog articles
12 content topics identified and briefed
1 collaborative storyboard editor (deployed to production)

None of this required the orchestrator to hold the entire context in memory. Each agent received a focused brief, worked independently, and reported a summary back.

Multi-Agent Skills

Once the three-layer structure is in place, skills can orchestrate complex multi-agent workflows without any added memory overhead. Our AIA course rewrite skill runs 11 parallel agents in two waves:

Wave 1 (parallel, 5 agents):
  - ResearchAgent    → finds current standards and competitor products
  - DraftAgent       × 3 → drafts three content sections concurrently
  - FactCheckAgent   → validates all product claims

Wave 2 (parallel, 5 agents):
  - ADAReviewAgent   → checks accessibility compliance
  - SEOAgent         → meta tags, schema, keyword density
  - LegalAgent       → regulatory claims audit
  - StyleAgent       → tone and reading level
  - CitationAgent    → formats all source notes

Wave 3 (sequential, 1 agent):
  - IntegrationAgent → merges all outputs, resolves conflicts, deploys

This entire workflow lives in the skill SKILL.md file. It contributes zero tokens to memory. It's only loaded when triggered.

Three-Layer Architecture for Claude Code — The foundation this article builds on
Building AI Agent Teams with OGSM — How to structure multi-agent teams with clear objectives