Skip to content
Agentic AI Systems

Agentic AI / MCP Architecture

Production-grade agents that operate against real business systems — designed around MCP and tool boundaries, scoped auth, eval harnesses, human escalation paths, and the rollout discipline production agents actually need.

Agent Architecture

Production

User / Caller

Agent Loop

plan · act · observe

Tools / MCP

Scoped tool manifest, deterministic schemas

Auth & Scopes

OAuth 2.0, PKCE, per-tool permissions

Evals & Traces

Pre-prod evals + live trace capture

Audit Log

Every call, every approval, replayable

Production exit

Agent ships behind a deterministic gate, not a vibe check.

Timeline
6-12 weeks
Engagement
Principal-led
Output
Production agent system
Time to Value
3-6 months

Who It's For

  • Teams automating complex multi-step workflows
  • Companies building AI-powered customer support
  • Enterprises with document processing needs
  • Products needing autonomous task completion

Our Approach

1

Architecture

Weeks 1-2

Map your workflows, define agent boundaries, identify tools and integrations, and design the orchestration layer with appropriate guardrails.

2

Core Build

Weeks 3-7

Build the agent framework with tool integrations, implement safety guardrails, human-in-the-loop escalation, and error recovery patterns.

3

Hardening

Weeks 8-10

Add monitoring, cost controls, rate limiting, and observability. Stress test edge cases and failure modes.

4

Production Launch

Weeks 11-12

Deploy to production with staged rollout, establish operational runbooks, and train your team on agent management.

Common Questions

How do you handle agent reliability and safety?

We build multi-layered guardrails: input validation, output filtering, action confirmation for high-risk operations, and automatic escalation to humans when confidence is low. Every agent action is logged and auditable.

What tools and frameworks do you use?

We're framework-agnostic and choose based on your needs. We work with LangGraph, CrewAI, custom orchestration layers, and direct API integrations. The architecture prioritizes reliability and observability over framework features.

How do you manage agent costs at scale?

We implement token budgets, caching layers, model routing (using cheaper models for simple tasks), and rate limiting. Cost monitoring dashboards give you real-time visibility into spend per workflow.

What You Get

  • Agent architecture with MCP/tool integration
  • Auth, permissioning, and auditability model
  • Eval harness and failure-mode test plan
  • Guardrails and safety framework
  • Human-in-the-loop escalation paths
  • Monitoring and observability pipeline
  • Cost controls and rate limiting
  • Deployment and scaling strategy

Agentic work moving from prototype to production.

A 2-week Architecture Diagnostic maps your workflow, tool boundaries, auth and eval needs, and the build sequence required to move from prototype agent to production system.