AI & Modern Builds
AI Agents That Actually Work in Production
Custom-built autonomous AI agents that handle real business workflows — not demos or proofs-of-concept, but production systems that run without babysitting.
The Problem
Most AI Agents Are Demos, Not Products
The AI agent landscape is full of impressive demos and shallow tooling. What's rare is a production AI agent that reliably handles real business workflows at scale — one that integrates with your actual systems, handles edge cases gracefully, and doesn't hallucinate its way into a customer service nightmare.
Building production AI agents requires more than an LLM API and some prompts. You need robust orchestration, tool integration, error handling, logging, human-in-the-loop checkpoints, and continuous evaluation of outputs. Most teams discover this the hard way — after deploying something that works great in testing and fails silently in production.
We've built production AI agents for sales automation, research pipelines, content workflows, customer support, and data processing. We know what makes the difference between a demo and a reliable system, and we build for the latter from day one.
Our Approach
Production-First AI Agent Architecture
We start by mapping the workflow you want to automate: what are the inputs, outputs, decision points, and failure modes? From that we design an agent architecture appropriate for your use case — single-agent for simple tasks, multi-agent with orchestration for complex workflows.
Every agent we build has observability built in: logging of inputs, outputs, tool calls, and token usage. We instrument for evaluation from the start, so you can measure agent quality and catch regressions. Human-in-the-loop checkpoints are designed in where the cost of an error exceeds the cost of a human review.
We use the best tools for each job: Claude, GPT-4o, or open-source models depending on your latency, cost, and capability requirements. We build on frameworks like LangChain or custom orchestration where those frameworks are the wrong abstraction.
Curious how this would work for AI & Modern Builds? — Send a quick message and we'll respond with specifics.
Deliverables
A Production AI Agent System
Custom Agent Architecture
A system designed for your specific workflow — not a generic template adapted to fit.
Tool & API Integration
Your agent integrated with the tools it needs: CRMs, databases, communication platforms, web browsers, and more.
Observability & Logging
Full visibility into agent inputs, outputs, tool calls, errors, and costs — so you know exactly what's happening.
Evaluation Framework
A suite of tests and metrics for measuring agent performance and catching regressions as you iterate.
Human-in-the-Loop Controls
Configurable checkpoints where human review is triggered based on confidence, task type, or output content.
Deployment & Scaling
Production deployment with queue management, rate limiting, and infrastructure that scales with your usage.
How We Work
From Workflow to Working Agent
- 1
Workflow Audit
Map the target workflow in detail: inputs, outputs, decision points, failure modes, and quality criteria.
- 2
Agent Design
Architecture selection, tool integration plan, prompt engineering strategy, and evaluation framework design.
- 3
Build & Evaluate
Build the agent, run against evaluation datasets, iterate until quality benchmarks are met.
- 4
Integration & Testing
Connect to your real systems and validate with real-world inputs before production.
- 5
Deploy & Monitor
Production deployment with monitoring, alerting, and ongoing evaluation to catch drift.
FAQs
Common Questions
Ready to start?
Ready to Build Something Great?
Let's talk about your product, your goals, and the fastest path to getting there. No pressure — just a real conversation.