07 — METRICS & DELIVERY

Capacity, Performance & Delivery

Platform capacity targets, LLM call profiles, milestone roadmap, risk mitigations, and phased delivery plan.

Capacity Planning

Platform Capacity Targets

25M
Requests / Year
Annual throughput target
6,500
Requests / Min Peak
~108 req/sec
≤ 15s
Submit p95
End-to-end latency
≤ 2s
Status p95
Cached/ready cases
30-50M
Historical Records
Supported dataset size
~1,625
Concurrent Active
In-flight requests at peak
99.9%
Availability SLA
Core intake & status
LLM Economics

LLM Call Profile

Each request type has a different LLM and tool call footprint, driving infrastructure sizing.

Flow LLM Calls / Request Tool Calls / Request Avg Latency
Submit Request ~6 ~18 (many parallelized) 12-15 sec
Request Status ~3 ~5 2-3 sec
~105M
LLM Calls / Year
~10
Peak LLM Calls/sec
~1,000
Avg Tokens / Call
Scaling

Scaling Levers

Queue-Depth Autoscaling

Pod/consumer count scales based on queue depth custom metrics in Kubernetes HPA.

API Response Caching

Repetitive validation responses cached in Redis to reduce redundant external API calls.

GPU Model Batching

vLLM continuous batching provides 2-4x effective throughput multiplier under concurrent load.

Scheduled Retries

Non-terminal dependency states deferred to retry queues with exponential backoff.

Delivery Roadmap

Milestones

Seven-phase delivery from foundation setup through production readiness.

Phase 1 — Foundation Setup

Networking, Kubernetes cluster, messaging infrastructure, and data stores provisioned.

Phase 2 — Rule Compiler & YAML Contract

SKILL.md parsing, YAML compilation, schema validation, and version management finalized.

Phase 3 — Submit Flow MVP

Core submit request flow with eligibility, form fill, documents, booking, and payment integrations. Baseline telemetry.

Phase 4 — Status Flow & Loop-back

Poll/push status updates, loop-back handling for incomplete requests, and status observability.

Phase 5 — Observability Hardening

Full trace audit, monitoring dashboards, alerting integration, and compliance reporting.

Phase 6 — Performance Validation

Load/performance testing at 6,500 req/min target throughput. GPU scaling validation.

Phase 7 — Production Readiness

Governance sign-off, security audit completion, runbook handover, and go-live.

Risk Management

Risks & Mitigations

Key risks identified during planning with corresponding mitigation strategies.

Circuit breaker per API + exponential retries + deferred queue for non-terminal states.

GPU autoscaling, continuous batching via vLLM, model routing between replicas, and KV-cache optimization.

Summarizer agent with configurable token thresholds + immutable trace store for full context recovery.

Automated rule tests + strict YAML schema validation + business team approval workflow before environment promotion.

Freshness SLAs per data source + periodic reconciliation jobs + alerting on stale-data thresholds.

Delivery Phases

Phase Roadmap

Phase 1 Foundation & MVP

  • Infrastructure and networking setup
  • Rule compiler and YAML finalization
  • Submit Request flow with core integrations
  • Baseline telemetry and tracing
  • Status flow with poll/push handling

Phase 2 Hardening & Scale

  • Full observability and audit hardening
  • Performance validation at target throughput
  • Security audit and compliance sign-off
  • Production readiness and governance approval
  • Operational handover and KT