AI Agents and RAG That Ship: Architectures, Tools, Pitfalls
Enterprises want AI agents that answer with authority, trace sources, and scale without drama. Retrieval-augmented generation (RAG) is the backbone, but production success depends on disciplined data plumbing, observability, and cost-aware design. Below is a pragmatic blueprint tying AI agents to PostgreSQL and MySQL development, Mobile analytics and crash monitoring setup, and the realities of scalable web apps.
Reference architecture that survives traffic spikes
Think in layers: source systems, indexing, retrieval, reasoning, guardrails, and feedback. Decouple so each layer can be swapped without rewiring the rest.
- Sources: product docs, tickets, chat logs, CRM, data warehouse snapshots.
- Indexing: chunking with semantic boundaries, embeddings, sparse signals (BM25), and metadata normalization.
- Retrieval: hybrid search (dense + lexical), filters by tenant, region, policy, and recency.
- Reasoning: LLM or small specialized models running via vLLM/Triton, orchestrated with Temporal for retries and compensation.
- Guardrails: prompt templates, system policy injectors, schema validators, PII scrubbing, and content moderation.
- Feedback: human review loops, analytics, and automated offline evals feeding continuous improvement.
Relational backbone: PostgreSQL and MySQL development
Vector stores are not your system of record. Use PostgreSQL or MySQL to anchor identities, permissions, document lineage, embedding jobs, and evaluation results. Practical patterns:
- Metadata-first: a documents table with doc_id, tenant_id, version, hash, source_url, legal_tier; an embeddings_job table tracking chunk counts, model version, and cost.
- Policy joins: authorize retrieval by joining retrieval candidates against ACL tables before the LLM sees text.
- Event journaling: append-only agent_events (agent_id, step, latency_ms, token_in/out, error_code) to power SLOs and cost insights.
With PostgreSQL, adopt pgvector for embeddings and RUM/GIN for lexical indices; keep ANN and filters in one query with approximate search and re-ranking. With MySQL, keep authoritative metadata and use an external vector engine (Qdrant, Weaviate, Pinecone) or HeatWave Vector; sync via CDC (Debezium) to maintain tenant fences.
Hybrid retrieval that earns trust
RAG quality dies when your chunking, indexing, or filtering is sloppy. Ship a hybrid design:
- Semantic + sparse: HNSW or IVF for dense; BM25 or SPLADE for sparse; reciprocal rank fusion to blend results.
- Attribution-first prompts: include top-k citations with stable doc_ids; the agent must justify each answer using retrieved spans.
- Time-aware recency windows: index time and version; prefer latest versions unless the question requests history.
- Self-checkers: ask the model to verify each claim against cited spans; drop claims that lack support.
Tooling that reduces drag
Pick boring, composable tools: LangChain or LlamaIndex for retrieval orchestration; Semantic Kernel for C# shops; OpenAI/Anthropic for hosted LLMs; vLLM for on-prem serving; Ray Serve for autoscaling; Temporal/Cadence for durable workflows; OpenTelemetry + Prometheus + Grafana for traces; Great Expectations; Sentry for agent exceptions; Trubrics or custom panels for human rating.
Mobile analytics and crash monitoring setup
Mobile agents need end-to-end observability. Treat prompts and outputs as first-class events, not logs you might read later.
- SDKs: instrument Segment or RudderStack, ship to Amplitude for funnels and to BigQuery/Snowflake for analysis; wire Sentry or Bugsnag plus Crashlytics for crashes.
- Event schema: app_session_id, user_anonymous_id, tenant_id, prompt_fingerprint, retrieval_doc_ids, latency_ms, token_usage, cache_hit, model_version, billable_flag.
- PII hygiene: hash identifiers at the edge; keep raw chats out of crash reports; redact with streaming filters.
- Offline resilience: queue analytics when offline; replay with backoff; cap payload size to avoid OS kills.
Designing scalable web apps for agents
Agents create bursty, stateful workloads. Solve scale with separation of concerns:
- Frontends post tasks to a queue; workers handle retrieval and reasoning; keep responses streamable via WebSockets or Server-Sent Events.
- Use Redis for short-lived state and idempotency keys; long-lived plans persist in PostgreSQL or MySQL.
- Autoscale workers by token rate, not request count; enforce per-tenant budgets and rate limits at the gateway.
- Multi-tenancy: namespace indices per tenant or shard by tenant_id; encode tenant filters in every retrieval call.
Security, governance, and pitfalls
Biggest failures stem from soft policy. Avoid these traps:
- Prompt injection: never pass raw retrieved HTML/Markdown directly; sanitize and strip scripts; constrain tools with allowlists.
- Data leakage: row-level security in PostgreSQL; view-based guards in MySQL; test with synthetic red-team prompts.
- Drift: pin model and embedding versions; re-index on upgrades; run A/B with holdouts before global rollouts.
- Hallucination: force cite-and-ground; refusal policies when confidence is low; surface confidence bands to UX.
Checklist to launch in 90 days
- Stand up relational backbone, pick vector strategy, define chunking and metadata.
- Instrument retrieval and agent events with OpenTelemetry; wire Sentry and analytics.
- Ship hybrid search, cite-and-ground prompts, and policy filters.
- Deploy autoscaling workers, per-tenant budgets, and end-to-end dashboards.
- Lock a golden set; run A/B; engage slashdev.io for rollout plans.
