This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Non-Conversational Experiences
What I Built
I built a deterministic, non-conversational PC building system that guarantees one core principle:
The system never produces an invalid PC build.
Instead of acting like a chat-based “PC expert,” this system behaves like an engineering validator embedded directly into the PC building workflow.
Users select components (CPU, GPU, case, PSU, etc.), and the system proactively:
- Validates compatibility using deterministic rules
- Detects hard conflicts (socket mismatch, size constraints, power limits)
- Refuses to guess when evidence is missing
- Offers evidence-backed, one-click repair suggestions
There is no back-and-forth conversation required.
The intelligence is injected directly into the workflow through state, rules, and retrieval, not dialogue.
This design choice is intentional.
For constraint-heavy, high-cost decisions like PC building, users don’t want an AI to “sound smart.”
They want guarantees.
Demo
🔗 Live Demo (Algolia Interface Demo):
https://dashboard.algolia.com/interface-demos/8c5cd5a3-1f6d-45b1-a8f7-024f47a5ec6e
What the demo shows
- Deterministic, non-conversational search over PC components
- Structured retrieval by brand, model, and chipset
- A UI surface designed for validation-first workflows
- Predictable, replayable results with no conversational ambiguity
This demo focuses on the retrieval and interaction layer of the system.
The deterministic compatibility rule engine runs locally and is intentionally separated from the UI.
How I Used Algolia Agent Studio
Algolia Agent Studio is used as a targeted decision assistant, not as a real-time conversational brain.
The agent is never responsible for final decisions.
Instead, it operates as a bounded advisor under strict constraints.
Indexed Data
I created two Algolia indexes:
1. parts index
- PC components (CPU, motherboard, RAM, GPU, case, PSU, etc.)
- Structured, faceted fields such as:
-
socket,ram_type,form_factor -
gpu_max_length_mm,cooler_height_mm -
tdp_w,watt
-
2. compat_rules index
- Engineering compatibility rules stored as structured records
- Each rule includes:
-
applies_to(component categories) -
severity(ERROR / WARN) - Human-readable explanation
- Repair strategy metadata
-
The Role of Agent Studio
Agent Studio is not triggered on every user interaction.
It is activated only when:
- A hard compatibility error occurs
- A user explicitly requests a “one-click repair”
- The system needs ranked alternatives under strict constraints
When triggered, the agent:
- Retrieves candidate components from Algolia
- Retrieves relevant compatibility rules
- Outputs strict JSON proposals only (no natural language)
- Never commits changes directly
All proposals must pass a local deterministic rule engine before being applied.
This separation ensures:
- No hallucinated decisions
- No circular dependency between AI output and system state
- No latency in basic validation paths
Why Fast Retrieval Matters
Fast, deterministic retrieval is what makes this system usable.
Algolia enables:
- Instant candidate retrieval under multiple hard constraints
- Faceted rule lookup with zero ambiguity
- Evidence-backed explanations through field-level comparison
Because retrieval is fast and structured:
- Validation feels immediate
- Refusals feel justified, not frustrating
- Repair suggestions feel intentional, not random
Without fast retrieval, the system would either:
- Guess (unsafe), or
- Stall (poor user experience)
Algolia makes it possible to refuse safely and recover instantly.
Determinism over Delusion
This project intentionally prioritizes correctness over cleverness.
The system refuses to produce a result when:
- Evidence is missing
- Constraints conflict
- No valid configuration exists
That refusal is not a failure—it is the product.
By combining deterministic rules with retrieval-powered suggestions, this project demonstrates how AI agents can be useful without pretending to be human, and without sacrificing trust.
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