Query Understanding, Routing, and Evaluation Systems

Challenge

Design systems that deeply understand user queries, route them to the correct answer types, tools, or agents, and continuously improve answer quality through robust evaluation and feedback loops.

The goal is to make intent, routing, and quality observable so that correctness, debuggability, and improvement velocity stay high; shipping a single agent sits outside the scope.

Framing the Unknowns

Key unknowns include:

• What users actually want versus what they ask for.
• Which answer types fit a query (factual, navigational, tool-based, agentic, unknown).
• When to use tools, agents, or static answers.
• How to measure answer quality beyond surface-level correctness.
• Where automation helps and where it obscures understanding.

The initial task is to expose intent, failure modes, and quality before optimizing behavior.

1. Objective and Success Definition

Objective

Build a system that can:

• Correctly interpret user intent.
• Route queries to the appropriate answer type, tool, or agent.
• Make answer quality measurable, inspectable, and improvable over time.

Success Criteria

• Queries route to the correct answer class within 1–2 steps.
• Teams can explain why a route or answer was chosen.
• Answer quality is measurable and comparable across versions.
• Regressions are detectable before user trust is impacted.
• Improvements are evidence-driven with explicit rationale, with intuition serving only as a hypothesis source.

2. Primary Risks and Tradeoffs (Before Implementation)

Technical Risks

• Misclassification of intent leading to incorrect routing.
• Overuse of agents where simpler answers suffice.
• Tool invocation failures masked by fluent responses.
• Evaluation metrics that reward verbosity over correctness.

Organizational Risks

• Teams shipping faster than they can understand failures.
• Debugging complexity discouraging iteration.
• Evaluation becoming performative rather than actionable.

Key Tradeoffs

• Accuracy vs. Flexibility: Hard routing rules reduce errors but limit adaptability.
• Autonomy vs. Inspectability: More agentic behavior increases opacity.
• Evaluation Depth vs. Velocity: Rich evals slow iteration but prevent regressions.
• Centralization vs. Team Ownership: Shared infra must stay lightweight and non-blocking.

These tradeoffs set the sequencing and scope.

3. Sprint-Oriented Phased Implementation Plan

Each phase earns additional autonomy by first making behavior observable and explainable. Later phases are gated by demonstrated understanding and readiness, with ambition alone insufficient.

Phase 1. Make Query Intent and Answer Types Explicit

Purpose Before routing or agents, define answer classes and when they apply.

Capabilities

• Define a small, explicit set of answer types.
• Classify incoming queries by intent and required answer type.
• Default safely when confidence is low.
• Log routing decisions with confidence and rationale.

What This Unlocks

• Visibility into what users are asking.
• Early detection of ambiguous or mismatched queries.
• A baseline for evaluation.

Risks Addressed

• Silent misrouting.
• Premature agent usage.
• Hidden failure modes.

Phase 2. Deterministic Routing and Tool Orchestration

Purpose Route reliably before introducing autonomy.

Capabilities

• Deterministic routing based on intent and answer type.
• Explicit tool invocation paths with preconditions.
• Clear separation between static answers, tools, and agents.
• Fallback paths when tools or agents fail.

What This Unlocks

• Predictable behavior.
• Easier debugging.
• Confidence in system boundaries.

Risks Introduced

• Overly rigid routing.
• Manual rule maintenance.

Mitigations

• Keep routing logic simple and auditable.
• Instrument uncertainty rather than forcing decisions.

Phase 3. Evaluation Infrastructure and Data Flywheel

Purpose Improve quality through measurement and observed data rather than guesswork.

Capabilities

• Capture structured traces of queries, routes, tools used, and outputs.
• Define evaluation criteria per answer type.
• Compare outputs across models, prompts, or routing strategies.
• Detect regressions and quality drift.

What This Unlocks

• Evidence-based iteration.
• Faster, safer experimentation.
• Shared language for “quality.”

Tradeoffs

• Increased infra complexity.
• Slower experimentation without discipline.

Phase 4. Agentic Evaluation and Debugging Tools

Purpose Enable teams to understand and improve complex behaviors.

Capabilities

• Replay and inspect full decision traces.
• Compare agent behaviors side-by-side.
• Simulate alternative routing or tool choices.
• Surface common failure patterns.

What This Unlocks

• Faster debugging.
• Safer introduction of autonomy.
• Higher team confidence in shipping changes.

Risks

• Tooling complexity.
• Analysis paralysis.

Mitigations

• Opinionated defaults.
• Focus on common failure cases.

Phase 5. Gradual Autonomy with Guardrails

Purpose Introduce agentic behavior only where it is earned.

Capabilities

• Conditional autonomy based on confidence and evaluation history.
• Human review for high-impact or low-confidence decisions.
• Continuous feedback into routing and evaluation layers.

What This Unlocks

• Scalable intelligence.
• Maintained trust.
• Sustainable velocity.

4. Technology Stack (Conceptual)

This challenge prioritizes architecture over specific tools.

Core components

• Query classification and routing layer.
• Tool and agent orchestration layer.
• Centralized trace and evaluation store.
• Inspection and comparison UI for teams.

Design principles

• Deterministic first, agentic second.
• Observable decisions at every step.
• Replaceable models and tools.
• Evaluation built in from the start, integrated instead of bolted on.

5. How I Would Classify This Challenge

This challenge evaluates:

• Ability to decompose intelligence into inspectable systems.
• Discipline around introducing autonomy.
• Understanding of evaluation as infrastructure and operational plumbing beyond reporting.
• Comfort balancing velocity with trust and debuggability.

Classification and ownership

• Classification: Principal-level or higher.
• Why: Success depends on cross-team alignment, infra design, and governance.
• Lowest level to own end-to-end: a strong Principal engineer with platform ownership, supported by leadership that prioritizes quality over speed.

The common thread: this is about building systems that let teams understand, trust, and improve intelligence over time; pursuing maximal autonomy sits outside this scope.