Language models wired to your tools, completing real multi-step work under guardrails you set. $3,999/mo retainer.
- Scope
- Build
- Evaluate
monthly retainer
Who this is for
You have a repeated task that involves several steps and a decision between them, and you want it handled without someone doing it by hand every time. AI agent development services means building that end to end: the model, the tool connections, the guardrails, and the deployment, so the result carries the task through to completion rather than stopping at advice.
The pain today
- A repeated multi-step task still needs a person to move information between systems by hand
- Existing automation follows a fixed path and breaks the moment a case does not match it
- No single person available to build and own the whole thing end to end
- Off-the-shelf agent builders stop at the first tool your process needs that they do not support
- No confidence the system will not take a costly wrong action once it starts running unsupervised
The outcome you get
- A system that carries a real task through to completion, not just to a suggestion
- Tool connections built for your actual process, not limited to a builder's preset list
- Guardrails and confidence thresholds agreed with you before it goes live
- A full log of every action taken, so results can always be traced and checked
- One senior engineer accountable for the build from design through deployment
What this actually does differently
A chatbot answers a question. A system built for real work does not stop there: it takes a goal, works out the steps needed to reach it, calls the tools required for each step, checks the result, and decides what happens next, until the task is actually done. The engineering effort sits in that loop: reliable tool calling, error recovery when a step fails, and a clear stopping point where it should hand off to a person instead of guessing.
A system that carries a real task through to completion, not just to a suggestion
Where this replaces manual work, and where it shouldn't
The clearest fit is a task with real steps and a real decision between them: reading an inbound lead and routing it to the right place, checking a queue and escalating what needs attention, processing a document and updating several systems from it. The wrong fit is a task that is genuinely a single lookup or a fixed sequence with no decision involved, where a simpler automation costs less and carries less risk. Part of the first conversation is being honest about which category your task falls into.
+500%: Lead base growth.
Guardrails before autonomy
A system that acts without limits is a liability the moment it takes a wrong step with real consequences. Every build starts with an explicit boundary conversation: which actions can run automatically, which need a confidence threshold cleared, and which always stop for a person to confirm first. Those boundaries are set before it goes live, and every action taken is logged so the reasoning behind it can always be checked.
Proof this holds up at real volume
The CRM I built for Norte Web Digital uses Claude AI to process inbound activity and route it through WhatsApp automatically, at a volume of 250 new leads a day, growing the lead base by over 500% with a lead-to-client cycle of three to four days. That is real commercial weight, not a demo answering a single test question.
Pricing, delivery, and ownership
AI agent development runs under the AI Development retainer at $3,999 a month, delivered in cycles of two to four days with daily async updates and a response inside 24 hours. The engagement carries a 14-day money-back guarantee, and you can cancel anytime after. Every tool connection, guardrail, and line of code is yours under Work Made for Hire terms from the day you pay.
Recent proof
A comparable engagement, delivered and documented.
A custom CRM that turned Google Maps into a lead machine
A custom CRM that captures leads from Google Maps, reaches them via WhatsApp, and uses AI to suggest replies. Lead base grew over 500%, 250 new leads a day.
Read the case studyRelated services
The same work, framed for a different situation.
Frequently asked questions
The questions prospects ask before they book.
A chatbot answers and stops. A system built for this takes a goal, plans the steps to reach it, calls the tools it needs, checks the result, and keeps going until the task is finished. The engineering difference is the loop wrapped around the model, not the model itself.
Explicit guardrails agreed before the build goes live. Low-risk actions can run automatically; higher-risk ones need a confidence threshold cleared or stop entirely for a person to confirm. That boundary is a design decision made with you, not left to the model to figure out.
Whatever your process actually uses: a CRM, a messaging platform, an internal database, a third-party API. The build is scoped around your real tools rather than a preset list a generic agent builder happens to support.
Yes, every action is logged with the reasoning behind it, so results can always be traced back and checked rather than treated as a black box.
It is a specific application of the same practice. This kind of build adds tool calling, multi-step planning, and guardrails on top of the model, retrieval, and deployment discipline that underlies every AI Development engagement.
The AI Development retainer is $3,999 a month, with a 14-day money-back guarantee and the option to cancel anytime after. Work lands in cycles of two to four days rather than a single delivery months out.