Agents that do work, not demos
An AI agent is an LLM running in a loop: it reads a goal, picks a tool, observes the result, and decides what to do next until the job is done. That loop is easy to demo and hard to run in production. The difference is engineering — scoped tool permissions, approval gates on consequential actions, tracing on every run, and evaluations that tell you when behavior drifts. DEV3LOPCOM, LLC is an Austin, Texas consultancy that builds agents to that production standard, and we build our own agent products, Bee and Canopys, so the lessons in our client work come from operating agents ourselves, not from slideware.
How we build an agent
We start by scoping the task honestly. Some workflows want a deterministic pipeline, not an agent; we will tell you which is which. When an agent is the right shape, we define its tool surface — the specific APIs, databases, and actions it may call, each with the narrowest permissions that still let it work.
Then we design the control layer. Consequential actions (sending messages, writing to production systems, spending money) route through human approval. Rate limits and spend caps bound the blast radius of a bad loop. Every run is traced end to end: which tools were called, with what arguments, and why.
Before anything ships, we build an eval suite from real task examples. Evals run on every prompt or model change, so “did the agent get worse” is a measured answer, not a feeling. Handoff includes the eval harness, dashboards, and runbooks so your team owns the system.
Failure modes we engineer against
- Agents that loop on a failing tool call and burn tokens until someone notices the bill.
- Prompt injection: content the agent reads (emails, tickets, web pages) trying to steer its actions. Tool scoping and approval gates keep injected instructions from becoming side effects.
- Silent regressions when a provider updates a model — caught by evals, not by your users.
- Over-permissioned tools where a summarization agent can somehow also delete records.
Why dev3lop
Agents are only as useful as the systems they can reach, and wiring agents into real infrastructure is data engineering. Our background building pipelines and APIs for clients like ExxonMobil (streaming data across 80+ campuses) and Nielsen (trillions of records) means the tool layer under your agent is built to the same standard as the agent itself. We route across model providers per task, so you are not locked to one vendor, and the whole team is onshore. If your use case is closer to grounded Q&A than autonomous action, look at our RAG systems practice; if you need model plumbing without the loop, see LLM integration.
Start with one workflow
The best first agent automates one well-bounded workflow with a human still in the loop. Bring us a candidate — ticket triage, data entry between systems, report drafting — and we will scope whether an agent fits and what the guardrails look like. Contact us to talk it through.