Working with the AI Architect

The AI Architect in Avylo is an active reasoning partner, not a generic conversational chatbot.

Instead of answering isolated prompts with speculative code, the AI Architect continuously reads your Project Knowledge, evaluates your Requirements, inspects the Canonical Architecture Model, and proposes structured, explainable improvements.

The Reasoning Workflow

Whenever you ask for architectural guidance or trigger an improvement analysis, the AI Architect follows a rigorous four-stage evaluation:

1. Context Gathering (Requirements + Current Model)
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2. Alternative Exploration (Identify 2–4 viable architectural patterns)
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3. Trade-off & Blast Radius Analysis (Performance, Complexity, Cost, Risk)
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4. Structured Proposal (Recommendation + Confidence + Evidence)

Anatomy of an AI Recommendation

Every proposal presented by the AI Architect contains:

1. The Core Recommendation

A clear statement of the chosen pattern or component (for example: “Use PostgreSQL with pgvector for initial hybrid relational + vector search workloads”).

2. Context & Motivation

Explains why this decision is relevant now, citing specific capabilities in your project.

3. Alternative Options

Details realistic alternatives that were evaluated (e.g., Dedicated Pinecone vector database, Elasticsearch cluster), explaining why they were not chosen as the primary recommendation.

4. Trade-Off Comparison

A side-by-side comparison across key dimensions:

• Operational Complexity: Setup overhead and maintenance burden.
• Latency & Throughput: Performance characteristics at target scale.
• Infrastructure Cost: Estimated baseline and scaling costs.
• Developer Velocity: Familiarity and tooling ecosystem.

5. Confidence Score

A calculated score representing the AI Architect's certainty based on the richness of your Project Knowledge and clarity of constraints.

Interacting with Recommendations

For every recommendation, you have full control:

• Accept: Applies the recommendation and updates the Canonical Architecture Model.
• Customize: Adjusts specific parameters, technologies, or deployment targets before applying.
• Compare Alternatives: Opens an in-depth comparison matrix between competing options.
• Skip / Dismiss: Archives the recommendation if it does not fit your current roadmap.