BiteByte: Multi-Agent Orchestration for 80% Latency Reduction

Overview

Why this project matters

BiteByte v2.0 extends my original meal-planning concept into a structured multi-agent system, achieving ~80% latency reduction (from 1min+ to ~20 seconds) by decomposing responsibilities across specialised agents. The architectural patterns were subsequently adopted for a commercial product at a major UK retailer.

Context

The problem

The original v1.0 used a single monolithic prompt chain that took over a minute to generate a complete meal plan with nutrition calculations and shopping lists. This was unacceptable for a responsive user experience. The redesign explored whether agent specialisation could reduce latency while improving output quality.

Architecture

How it was built

The system separates concerns across four specialised agents: intent handling (parsing user dietary requirements), nutrition rules (applying constraints and calculations), meal composition (generating recipes and combinations), and shopping-list generation (aggregating ingredients). Each agent has a focused prompt and operates on a smaller context window, enabling parallel execution where possible. Input validation and security controls prevent prompt injection.

Outcome

What was delivered

~80% latency reduction: from 1min+ down to ~20 seconds for a complete meal plan. The agent specialisation also improved output quality — each agent produces more consistent results with a narrower responsibility.

Commercial adoption

Real-world impact

The multi-agent orchestration patterns demonstrated in BiteByte were subsequently adopted by a separate team to accelerate the development of a Recipe Builder tool for a major UK retailer. The architectural approach — separating responsibilities across intent handling, constraint validation, composition, and output generation — proved directly transferable to a commercial product context. It is rare for a consultant's personal R&D to directly influence a client's product roadmap. This adoption validated the architectural thinking behind BiteByte and demonstrated that the agentic patterns scale beyond the original use case.

Retrospective

What I would do differently

The biggest win wasn't parallelisation (which helped) but reducing context window size per agent. Smaller, focused prompts generate faster and more reliably. This mirrors the microservices insight: separation of concerns improves both performance and maintainability.

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