Presentation: Batch Intelligence at Scale: Cost-Efficient Multi-Agent LLM Workflows with Built-In Resilience

7.5 relevance

Walmart's multi-agent LLM workflows for batch inventory is directly relevant to AI agent orchestration.

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Why It Matters

For a platform engineer focused on production AI, this provides a concrete architectural playbook for cost-efficient, fault-tolerant multi-agent orchestration in a high-stakes retail environment.

Key Takeaways

Adopt MCP-based grounding and loop agents for resilience, and switch from PTUs to pay-as-you-go LLM inference to reduce batch processing costs by 90%.

Presentation: Batch Intelligence at Scale: Cost-Efficient Multi-Agent LLM Workflows with Built-In Resilience

Summary

Walmart Global Tech deployed multi-agent LLM workflows across 4,700 stores for nightly batch inventory forecasting, using MCP grounding and loop agents to handle schema violations, cross-department data mismatches, and network drops. By shifting from provisioned throughput units (PTUs) to pay-as-you-go inference, they slashed cloud costs from over $100k to $10k per month while maintaining resilience at scale.

Author

Aditya Mulik