Reference architectures | security baselines | implementation patterns
Agent Architecture for Production AI Systems
Multi-agent RAG systems, MCP servers, and enterprise identity—built with the same security rigor we honed across large-scale SaaS platforms. Fifteen years of production engineering applied to AI platforms that actually ship.
Use these patterns to vet your current architecture or bring us in for a dedicated architecture review when you're ready to harden the build.
This architecture supports Retrieval-Augmented Generation (RAG) using semantic search, metadata filters, and tenant-aware retrieval. It’s designed for multi-tenant SaaS platforms that need secure vector search, agent tool-calling guardrails, and LLM safety layers.
Enterprise-grade AI platform reference architecture.
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Where these apply
When to use these architectures (and when not to)
Use these patterns when you need production-grade retrieval, semantic search, and agent orchestration under multi-tenant or enterprise constraints. If you want a quick POC, a lighter diagram is fine—these are for systems that must pass security reviews and keep auditors comfortable.
Best for
- Multi-tenant SaaS with RAG and semantic search in production.
- Agent/MCP workflows that touch real customer data or tools.
- Teams facing vendor security reviews or investor diligence.
If this is you, proceed
- You need diagrams + implementation notes you can defend to a security team.
- You care about retrieval safety as much as model quality.
- You want agents with rollback, RBAC, and audit trails from day one.
When not to use
- Short-lived demos that won't handle real user data.
- Single-tenant prototypes without compliance or uptime requirements.
- Projects that don't need semantic search or RAG—use a simpler pattern.
Multi-agent systems with zero data leaks
Multi-Agent RAG Systems with Tenant Isolation
Agent architecture gets messy fast when you're serving multiple customers. Every tenant needs isolated ingestion, storage, retrieval, and evaluation pipelines. No shared embeddings, no cross-tenant leakage, no excuses.
We separate every layer of the retrieval pipeline. pgvector with HNSW indexing for hybrid search, enforced row-level security in Supabase, and per-organization embedding namespaces that prevent cross-tenant contamination.
Evaluations run on isolated sandboxes. Source documents track lineage automatically, so every agent response includes citations with traceable provenance. Observability hooks surface drift, hallucinations, and prompt injection attempts before customers see them.
Why this matters: when Series A diligence or enterprise buyers ask how you isolate tenants, you answer with diagrams and logs—not hand-waving or last-minute rewrites.
- Isolated ingestion pipelines with queue-level separation per organization.
- Per-tenant vector stores + Postgres schemas with RLS enforcement.
- Citation tracking and lineage metadata for every document chunk.
- Streaming assistants with eval harnesses monitoring drift + cost.
- Automated AI security testing prior to launch.
Secure agents touching production systems
MCP Server Development for Secure Tool Execution
Model Context Protocol servers are where agent architecture can break badly. Tools call external APIs, mutate state, and run workflows that should never execute without tight authorization. We treat every tool like a privileged integration.
Why this matters: a single insecure MCP tool can leak customer data or drain API budgets. These controls keep auditors and security teams comfortable opening agents to production systems.
- Typed tool definitions (TypeScript + Zod) with enforced schemas.
- RBAC-aware execution with scoped credentials per user + agent.
- Fine-grained rate limiting and dead-letter queues when tools fail.
- Audit logging for every invocation with webhook replays.
- Rollback workflows to reverse unwanted tool actions.
We run penetration testing services against each MCP deployment. Prompt injection, context poisoning, privilege escalation-every scenario gets simulated.
Logs stream to our observability stack, so you know who ran what tool, when, and with which parameters. No black boxes.
Security controls before the first user
Production Security Baselines for AI Platforms
Security isn't some post-launch audit. Agent platforms ship with guardrails already mapped to zero-trust principles. Every layer-UI, API, vector store, inference pipeline-gets hardened before production traffic.
Why this matters: compliance reviews and investor diligence now happen pre-launch. With baselines documented up front, you sail through questionnaires instead of scrambling after a red flag.
Prompt injection testing (automated + manual) is wired into CI. XSS/CSRF protections wrap every streaming response. PII detection workflows monitor embeddings, ensuring you aren't leaking regulated data into vector stores.
Infrastructure as code keeps environments consistent. Dev/staging/prod have guardrails for env vars, credentials, and role assumptions. Observability covers cost tracking, latency, model drift, and anomalous agent behavior.
- Prompt injection simulations across all agent instructions.
- Environment-specific guardrails + infrastructure policies.
- PII detection for vector ingestion + retrieval flows.
- Zero-trust network configs + edge validation.
- Automated vulnerability scanning tied to deployments.
Full-stack, production ready
Full-Stack Implementation on Modern Infrastructure
We ship the entire agent platform-not just AI demos. Frontend, backend, billing, observability, and deployment automation land together. Built with the same rigor we apply to enterprise-grade SaaS platforms.
Why this matters: you launch with billing, analytics, and rollback plans in place, so investors see a business-ready platform instead of a hackathon project.
- Next.js 15+ with server components for streaming inference.
- Vercel edge runtime for low-latency agents + queue workers.
- Supabase for auth, database, and real-time subscriptions.
- Clerk + enterprise SSO/SAML integrations.
- Stripe for usage-based billing + credit systems.
Sentry, custom telemetry, and regression suites track performance across every agent. Cost dashboards monitor inference spend per organization. Eval harnesses measure accuracy and drift automatically.
The same engineering rigor drives our AI platform development services: no junior teams, and the same engineer leads every layer of the build.
Enterprise identity and governance
Enterprise Identity and Multi-Tenant Organization Management
Enterprise AI platforms live or die on identity. We handle SSO, SCIM, RBAC, and multi-tenant hierarchies so agents respect org boundaries and compliance requirements.
Why this matters: every enterprise buyer asks about SSO, provisioning, and audit trails before they sign. These patterns give you screenshots, logs, and contracts-ready answers.
- Clerk-powered auth with custom domains + enterprise SSO.
- SCIM provisioning for automated user + group sync.
- RBAC across agents, datasets, workflows, and tools.
- Session management with configurable idle + absolute timeouts.
- OAuth flows so agents only access the resources they need.
Usage-based billing ties credits to roles. Admin dashboards show spend, agent performance, and security events. Compliance reporting maps identity events to audit logs with clear evidence trails.
When auditors ask, you'll have real answers backed by telemetry-not guesswork.
Why CTOs bring me in
Architect, implementer, and security tester
Architecture + code
You get diagrams, schemas, and the working code—not a slide deck handed to another team.
Security baked in
RAG retrieval filters, tenant isolation, prompt-injection suites, and MCP tool guardrails are part of the plan, not an afterthought.
Production mindset
Billing, observability, rollback, and compliance evidence ship with the architecture so you can launch and defend it.
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We design and build agent architectures that survive real production traffic. Security built in from day one. Enterprise identity, tenant isolation, observability, and compliance already handled.