Custom AI agent development for operations-heavy teams

The interesting question is not "can an AI agent do this task." Most can, in a sandbox. The question is whether it holds up across the messy long tail of real inputs, integrates with the tools your team actually uses, and stays inside a cost and accuracy budget you can defend to a CFO.

We treat agent work as an operations problem first and an AI problem second. You bring a process that consumes hours of skilled attention (support triage, data reconciliation, research synthesis, real-time monitoring). We build the agent that owns the predictable 80% and hands the genuinely ambiguous 20% back to a human with full context attached.

My Read: The agencies selling "autonomous everything" are optimizing for the demo, not your P&L. A narrow agent that reliably clears one queue is worth more than a general one that constantly needs a babysitter.

What We Build

Each block below is a deployable unit of work. We scope one before we promise five.

1. Custom AI Agents on a Hardened Scaffold

We build your business logic on top of an internal agent scaffold we maintain: the agent loop, tool-calling layer, retry and fallback handling, structured-output validation, and tracing are already solved and tested. Your budget goes to the part that is actually yours—the reasoning and the integrations—not to reinventing the loop.

Fact: This is the credible version of a "reusable codebase." You get a custom agent; we do not rebuild the plumbing on your dime every time.

2. Model Context Protocol (MCP) Integration

We connect agents to your real tools and data through MCP servers rather than brittle one-off API glue. Fact: Model Context Protocol is an open standard for exposing tools, resources, and data to an LLM through a consistent server interface. In practice, that means your CRM, your database, your internal services, and platforms like n8n become callable capabilities the agent can use, with permissions and audit trails you control.

The payoff is durability. When you swap a downstream tool, you update one MCP server, not every prompt in the system.

3. Token and Context-Window Optimization

Inference cost is a line item, not a footnote, so we engineer for it from day one:

• Context Handling over Context Stuffing: We retrieve and scope only the context a step needs, then compact running history into summaries rather than dragging the full transcript through every call.

• Model Routing: A small, cheap model triages and classifies; the expensive reasoning model is invoked only when the task earns it.

• Prompt Caching: Stable instructions and reference context are cached so repeated calls bill at the reduced cache-read rate instead of full input price.

• Structured Outputs: Schema-validated responses cut the retry loops that quietly double your token spend.

My Read: On a high-volume agent, disciplined context and routing work is usually the difference between unit economics that close and ones that do not. This is where most "it works but it is too expensive" projects are won or lost.