Five AI Agents, One Fix Plan
A multi-agent LLM audit that scores six GEO categories and writes the remediation report
Checklists find symptoms. This system finds which weaknesses matter. Five specialist AI agents review a site, a weighted model scores it, and the report proposes a fix per issue.
What the team needed
Some sites fail not on one broken item but on mixed signals across content, authority, structure and platform footprint. Rules engines list symptoms; generic AI SEO tools return a wall of advice. The team needed a panel of specialists and one report they could act on the same day. We built it as the second layer after the scored audit tool. Five specialised agents each own a slice of the problem; a synthesis step scores six weighted GEO categories and writes a report with every issue, why it matters and the fix.
Constraints we had to design around
- 01
No agent overlap
Five agents only help if each owns a distinct perspective. Otherwise it is expensive repetition dressed up as AI.
- 02
Scoring qualitative signals consistently
Citability, authority and E-E-A-T are not mechanical checks. The model had to turn judgement into numbers comparable across categories.
- 03
Five outputs, one plan
Independent agent outputs are useless as five essays. A synthesis layer had to merge them into one master report.
- 04
Every finding actionable
Deep audits drift into verbosity. Each finding had to trace to evidence, impact and a concrete next step.
Architecture and what we shipped
- 01
Five agents with defined responsibilities
Each agent reviews its own slice of the evidence, so the system compares perspectives instead of repeating the same commentary five times.
- 02
Six weighted GEO categories
AI citability, brand authority, content E-E-A-T, technical GEO, schema and structured data, and platform optimisation. Interpretable by design.
- 03
Weighted score, not a flat checklist
Not every issue costs the same visibility. Weights show whether the bigger problem is authority, thin content, missing schema or extractability.
- 04
Synthesis layer and report file
The final step groups issues by theme, records score impact, explains why each matters and proposes the fix. A working document, not a summary.
- 05
Paired with the fast audit
The lighter tool handles baselines and repeat runs. The multi-agent audit takes over when a site needs wider reasoning and a strategic plan.
Shipped in three phases
Audit model first, then agent orchestration, then scoring and report synthesis.
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Phase 1
Audit model
Agent roles, scoring framework and GEO category definitions
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Phase 2
Agent orchestration
Multi-agent review flow, evidence gathering and response normalisation
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Phase 3
Scoring and reporting
Weighted GEO score, issue consolidation and the proposed-fix report file
Sample output
Specialised AI agents reviewing the site from different angles
Weighted GEO categories consolidated into one score model
Overall GEO score in a sample deep audit after weighted category scoring
Weighted GEO score breakdown
A sample run. The table shows where citation risk concentrates before implementation starts.
| Category | Score | Weight | Weighted score |
|---|---|---|---|
| AI Citability | 42/100 | 25% | 10.50 |
| Brand Authority | 22/100 | 20% | 4.40 |
| Content E-E-A-T | 42/100 | 20% | 8.40 |
| Technical GEO | 79/100 | 15% | 11.85 |
| Schema & Structured Data | 32/100 | 10% | 3.20 |
| Platform Optimization | 38/100 | 10% | 3.80 |
| Overall GEO Score | 42/100 |
Questions about multi-agent AI systems
Why use several AI agents instead of one big prompt?
One prompt blends every concern into generic advice. Separate agents with separate evidence and responsibilities produce findings that can be compared, weighted and traced back, which is what makes the final report trustworthy.
How is this different from the lighter audit tool?
The lighter tool is a fast, rules-based baseline. The deep audit adds multi-agent reasoning, weighted GEO scoring and a full remediation report for sites with authority or content problems.
What is in the final report file?
Issues grouped by category, why each one matters, its score impact and a proposed fix, so developers and content teams can start implementation straight from the document.
Can you build a multi-agent LLM workflow for a different domain?
Yes. The pattern of specialist agents, a normalisation step, weighted scoring and a synthesis report applies to compliance reviews, document analysis or due diligence. We scope it on a free call and quote a fixed price.
Service: AI development
Next project: One Audit Run, One Score, Two Reports
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