Integration

Evaluate tool/agent configuration for capability bloat

Detect oversized toolkits and agent scopes that degrade tool selection, raise blast radius, and hide unused integrations — then shrink, merge, or split by role before adding more prompt text.

Analyze authentication and authorization requirements to identify security gaps

Map AuthN vs AuthZ across UI, agent, and tool principals; find gaps where identity succeeds but permission, least privilege, or verified resource binding is missing.

Evaluate accuracy-latency trade-offs and justify configuration decisions

Justify model, retrieval depth, caching, and tool fan-out against an explicit accuracy floor and latency/cost SLA — quantify deltas and reject one-sided wins that break contracts.

Analyze observability challenges and select monitoring strategies at scale

Instrument LLM-specific SLIs (quality, cost, latency, tool errors), sample traces strategically at high volume, redact secrets, and correlate traces with user outcomes and runbooks.

Design a RAG pipeline with appropriate chunking and indexing strategies

Design structure-aware chunking, provenance metadata, and freshness-safe index promotion so retrieval returns complete, citable units matched to document shape.

Apply retrieval strategies matched to data shape and query pattern

Classify queries (exact ID, paraphrase, filtered browse) and match dense, sparse, hybrid, and structured filters — evaluate per pattern, not only macro averages.

Evaluate connection protocols and select the appropriate integration mechanism (MCP, API/CLI, agent-to-agent)

Choose MCP for shared tools/resources across hosts, API/CLI for owned narrow paths, and agent-to-agent when role isolation and explicit handoffs matter — never secrets-in-prompts.

Evaluate progressive discovery vs. monolithic context strategy

Prefer progressive discovery for large or changing corpora; reserve monolithic (cached) context for small stable packs that fit with headroom — and pin transactional facts outside rolling summaries.