Quality and performance improvements

Problem

Agent interactions via the MCP server suffer from quality and performance issues that directly impact the user experience: tool-call latency is variable and sometimes exceeds agent timeout thresholds, rendered dashboards occasionally contain layout or data errors that the agent cannot detect, and prompt-guided workflows produce inconsistent results across different LLM providers. These issues are known anecdotally but lack measurement — there are no benchmarks for tool-call p50/p95 latency, no quality scores for agent-generated dashboards, and no A/B framework for comparing prompt strategies. Without measurable baselines and targeted improvements tied to user-facing outcomes, quality work is unfocused and unverifiable.

Context

• Once AI agent tool interfaces, execution workflows, and eval-driven behavior tuning is in regular use, quality and performance work needs to target the actual slow, flaky, or costly paths rather than generic optimization ideas.
• The right scope here is evidence-driven: identify bottlenecks, remove the highest-friction issues, and make sure the fixes are measurable and regression-resistant.
• Expected touchpoints include dataface/ai/, MCP/tool contracts, cloud chat surfaces, eval runners, and prompt artifacts, telemetry or QA evidence, and any heavy workflows where users are paying the cost today.

Possible Solutions

• A - Tune isolated hotspots as they are reported: useful for emergencies, but it rarely produces a coherent quality/performance program.
• B - Recommended: prioritize measurable bottlenecks and quality gaps: couple performance work with correctness and UX validation so improvements are both faster and safer.
• C - Rewrite broad subsystems for theoretical speedups: tempting, but usually too risky and poorly grounded for this milestone.

Plan

1. Identify the biggest quality and performance pain points in AI agent tool interfaces, execution workflows, and eval-driven behavior tuning using real usage data, QA findings, and support feedback.
2. Choose a small set of improvements with clear before/after measures and explicit user-facing benefit.
3. Implement the fixes together with regression checks, docs, or operator notes wherever the change affects behavior or expectations.
4. Review the measured outcome and turn any remaining hotspots into sequenced follow-up tasks instead of leaving them as vague future work.

Implementation Progress

Review Feedback

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