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Services / AI Agents & MCP

AI agents and MCP servers built for production

Agentic AI systems and Model Context Protocol (MCP) servers with authentication boundaries, audit logging, and security testing from day one. Agents that reason, call tools safely, and survive investor diligence.

Explore PromptWheel Review AI security baseline

Fit check

You're a fit if…

Production stakes

Agents will touch real systems (billing, support, data pipelines) and must be auditable with rollback and RBAC.

Retrieval + tools

You need semantic search/RAG plus tool calls in one flow, with clear tenant filters and schema validation.

One owner

You want architecture diagrams, MCP server code, and security testing handled by the same team.

When you actually need agents

AI agents are LLM-powered workflows that keep state, call tools, and make decisions across multiple steps. They aren't always the right fit — simple prompts often work. Agents make sense when you need:

  • Multi-step workflows where a single prompt can't manage state or secure MCP tool usage
  • Human-in-the-loop approvals with audit trails
  • Chained RAG retrieval, tool execution, and policy checks without brittle scripts
  • Long-running processes (hours/days) orchestrated across tasks

Use cases we build

Common use cases for custom AI agents
Customer support automation
Agents triage tickets, summarize context, and draft responses with human approval.
Document analysis & processing
Automated workflows digest PDFs, extract structured data, and push into CRMs or ERPs.
Research & data gathering
Agents crawl knowledge bases, run retrieval, and compile briefings for internal teams.
Workflow automation
Connect Jira, GitHub, Stripe, or HubSpot so agents update systems after reasoning steps.

How we build agents & MCP servers

Typed tools, rate limiting per tenant, runtime sandboxes, and audit logging make MCP servers production-ready.

  1. 1. Workflow mapping. Identify user journeys, desired automation, and human-in-the-loop steps.
  2. 2. Tool surface design. Define MCP tools/functions, schemas, auth scopes, and failure modes.
  3. 3. Build & guard. Implement agents, prompts, tool servers, and guardrails. Wire logging + alerts.
  4. 4. Hardening & rollout. Prompt-injection testing, pen tests, telemetry dashboards, and staged rollout.

Technical stack

Next.js with App Router, Vercel Edge for low-latency routing, Postgres + pgvector for agent memories, Redis/Upstash for queues, Docker sandboxes for code execution, PostHog for every tool invocation. LangChain, Anthropic Claude, OpenAI, and custom orchestrators all wired with RBAC, logging, and guardrails.

Our security testing applies to every MCP server — we attack tool definitions with malicious prompts, test privilege escalation, and validate audit logs.

Case study

Multi-tenant MCP API platform

Built a multi-tenant SaaS platform with MCP server integration, Clerk authentication, rate limiting, and production infrastructure on Vercel with comprehensive security testing.

Technical challenges

  • Row-level security for multi-tenant data isolation
  • Redis rate limiting per tenant + per tool
  • MCP server integration with typed auth boundaries

Key outcomes

  • 2,000+ automated tests from the first sprint
  • Auth boundaries prevent tenant data leakage
  • Zero-downtime production deployment

8 weeks

Build time

2,000+

Security tests

Multi-tenant

Architecture

Common questions

How do AI agents differ from simple prompts?
Prompts are great for single-turn responses. Agents maintain state, call tools, log actions, and support human approvals. We recommend agents only when workflows truly need those behaviors.
What is the typical timeline for agent development?
Most production-ready agent builds take 6-8 weeks including discovery, tool design, guardrails, observability, and penetration testing. Smaller pilots can ship faster if prerequisites exist.
How do you secure agent tool access?
Every MCP tool has RBAC, OAuth scopes, per-tool API keys, rate limiting, prompt-injection defenses, and logging to SIEM/PostHog. We also add runtime sandboxes when tools execute code.
Do you build custom MCP servers or use existing ones?
Both. We build custom MCP servers when you need typed schemas, tenant-aware logic, or internal APIs. We also integrate Anthropic Claude MCP or LangChain routers when they fit.

Learn more about AI agents

Explore our tools, read in-depth guides on agent architecture, or get in touch to discuss your use case.

Read the agent architecture guide Review broader platform Get in touch