AI Agent Engineering
AI Agents That Work Beyond the Demo
Custom agent development with the orchestration logic, tool integration, and reliability engineering that production demands.
Beyond Chatbots
AI agents are autonomous systems that reason, use tools, and accomplish multi-step tasks without constant human guidance. Unlike simple chatbots that respond to prompts, agents can research, analyze, execute actions, and coordinate with other systems.
The promise is significant: agents that handle complex workflows your team currently manages manually. The challenge is building agents that work reliably, not just impressively in demos.
What Agent Engineering Involves
Building production-grade AI agents requires more than calling an LLM API. It requires:
Orchestration Design
How the agent decides what to do next, what reasoning patterns it follows, and when it uses tools versus responds directly.
Tool Integration
Agents derive power from their ability to use tools—querying databases, calling APIs, reading documents, executing code. Each integration must be reliable and handle errors gracefully.
Memory and Context
Production agents need robust memory systems that maintain conversation history, recall relevant context, and know the limits of their knowledge.
Multi-Agent Coordination
Complex workflows often require multiple specialized agents working together—coordinating handoffs, error handling, and output quality.
The Production Gap
Most agent implementations fail not because the underlying model is incapable, but because:
- Edge cases break orchestration logic. The agent loops infinitely, calls the wrong tool, or produces gibberish when encountering unexpected inputs.
- Error handling is an afterthought. When tools fail or return unexpected results, the agent doesn't know how to recover.
- Context management degrades. Long conversations overwhelm context windows. Critical information gets lost.
- Evaluation doesn't exist. There's no systematic way to know if the agent is getting better or worse over time.
We engineer agents with these failure modes in mind from the start—building in circuit breakers, fallback behaviors, and evaluation harnesses that catch problems before users do.
Frameworks We Use
We're framework-agnostic and select based on your specific needs. The code runs in your environment—no framework creates vendor lock-in.
LangChain
Flexible orchestration with extensive tool ecosystem. Good for complex, custom agent designs.
CrewAI
Multi-agent coordination with role-based design. Good for workflows requiring specialized agents.
AutoGen
Conversational multi-agent patterns. Good for collaborative reasoning tasks.
Custom implementations
When frameworks add complexity without value, we build lean orchestration directly.
Our Approach
Discovery
Understand your workflow in detail. What tasks should the agent handle? What decisions require human escalation?
Architecture
Design the agent's reasoning patterns, tool integrations, and memory systems. You approve before we build.
Implementation
Build the orchestration logic, integrate tools, and configure memory. Iterate until it handles your test cases reliably.
Evaluation
Create testing harnesses that measure agent quality. These harnesses run automatically, catching regressions.
Hardening
Add error handling, logging, and monitoring. The agent knows how to fail gracefully.
Handover
You receive all orchestration code, tool integrations, and evaluation harnesses.
Building AI agents that need to work in production?
Let's discuss your use case, map the orchestration requirements, and show you what reliable agent engineering looks like.