Gemini 3.1 Pro in Multi-Agent Systems: High Design Quality, 20% Tool-Call Failure Rate

Architecture and Testing Context

The team behind Bobr, an AI presentation generator, tested Gemini 3.1 Pro within a two-level agent system. The architecture consists of:

• Orchestrator Agent: Handles conversation, understands user intent, plans structure, and dispatches work via tool calls.
• Creative Agent (Gemini 3.1 Pro in this test): Receives slide descriptions, generates images, builds templates (1920x1080), and returns results via a submit_slide tool call.

The creative agent has tools including generate_image, search_images, and submit_slide. The submit_slide call is critical—it returns a 'submit' signal, terminates the agent loop, and extracts slide data. Both agents run through the same loop with streaming, parallel tool execution, and iteration limits.

Strengths: Design and Aesthetic Output

When Gemini 3.1 Pro works correctly, it produces superior design output compared to other models tested (Claude Sonnet 4.6 and GPT-5.2). Specific strengths include:

• Aesthetic intuition: Better color theory and visual hierarchy.
• Layout creativity: Experiments with asymmetric compositions, overlapping elements, and modern UI styles like dark-mode/glassmorphism.
• Vibe interpretation: Effectively handles vague prompts like "make it feel premium" or "tech startup vibes."
• Code quality: Generates modern, structural HTML/CSS.

Critical Problems in Production

The team encountered two major reliability issues with Gemini 3.1 Pro in their agentic pipeline:

1. ~20% Tool-Call Failure Rate

In approximately 20% of requests, Gemini 3.1 Pro fails to call the required submit_slide tool. Instead, it exhibits several failure patterns:

• Outputs raw HTML template as plain text, describing what it "would" create rather than triggering the tool.
• Generates images correctly but stops without submitting, hitting iteration limits.
• Calls image generation tools but writes natural language summaries ("Here is your beautiful slide...") instead of the final tool call.
• Enters loops refining design descriptions in text without committing to action.

Since submit_slide is the hard exit path, failures result in no data returned to the orchestrator and failed user generations.

2. Garbled/Corrupted Output

The model frequently returns corrupted text in responses—random character sequences, broken Unicode, half-encoded strings. This corruption sometimes bleeds into slide content (variable values, template markup), meaning even successful submissions might display gibberish text in presentations.

Comparison with Other Models

• Claude Sonnet 4.6: Near-zero failure rate on submit_slide calls in the same creative agent role, described as "boringly reliable" with no garbled output.
• GPT-5.2: Moderate tool reliability between Gemini and Claude, but doesn't suffer from encoding/gibberish issues.

Attempted Mitigations

The team tried several approaches without significant improvement:

• Adding aggressive explicit instructions in system prompts: "You MUST call submit_slide. Do not output the template as text."
• Injecting few-shot examples showing exact expected tool-call patterns.
• Reducing iteration limits to force faster convergence.
• Stripping down and simplifying tool schemas.

Despite these issues, Gemini 3.1 Pro remains live in their system due to its superior design capabilities when it functions correctly.