What shows up between the pilot and production.
Architecture, cost and governance for AI agents, written for the people who will decide and for the people who will sustain it afterwards. No invented numbers and no promises the operation does not confirm.
What a Forward Deployed engineer is, and why enterprise AI depends on one
It is the engineer who builds inside the client's operation, with the real systems and the team that will run it, and measures the work by the result. The role was born at Palantir and became the model AI companies use to get out of the pilot stage.
Read the articleWhere to start with AI in large enterprises: the first decisions
The first step with AI is not choosing the tool. It is choosing a process, naming who answers for it and measuring how it works today. Everything else depends on that order.
Read the articleAI for car dealerships: what to integrate with the DMS and the CRM
At a dealership, an AI agent is worth what it can read and write in the DMS and the CRM. The design that works starts with knowing where each piece of data lives and what the agent must never do on its own.
Read the articleAI in healthcare and the LGPD: how agents protect sensitive data
In healthcare, almost every piece of data the agent touches is sensitive. What the LGPD requires of health plans, hospitals and clinics, and the controls that protect the data in each flow.
Read the articleLegal automation with AI agents: a guide for law firms and legal departments
AI agents work in legal when the line is clear: what produces a fact goes to the agent, what produces an opinion about the client's rights stays with the lawyer. The rest of the design follows from that.
Read the articleWhy AI projects die in the PoC and never reach production
The PoC proves the model can do it. Production requires that the operation can do it. Those are different questions, and the second is almost never asked before the first is answered.
Read the articleWhat AI agent orchestration is, and why companies pay for it
A model answers. An agent acts. Orchestration is what makes the agent act inside a process, with state, tools, limits and a record. It is the part that does not show up in the demo.
Read the articleHow to evaluate an AI agent platform: the eight criteria
There is no best platform. There is the one that answers eight questions correctly for your case. Here are the eight, and what a poor answer usually hides.
Read the articleAI agents on WhatsApp: what works in a real operation
In Brazil, the customer is on WhatsApp. The agent that works there is not the one that answers everything: it is the one that resolves what it can, passes the rest to the right person and does not lose the context on the way.
Read the articleIntegrating AI agents with the ERP, CRM and WhatsApp without a long project
Integration is where the schedule blows up, and almost never because of the agent. A ready connector solves the easy part. The other four parts decide whether the project takes weeks or quarters.
Read the articleBuild on a framework or buy a platform: the full calculation
The model is the smallest part of the work. The right decision depends on looking at the rest: who sustains it, what happens on the second agent, and what the audit will ask for.
Read the articleCode-first or no-code for AI agents: the wrong choice
This is not a choice between two tools. It is a choice about who builds what, and about what happens when the business's prototype has to become IT's code.
Read the articleAI agent observability: what to record, and why
In ordinary software, the log records what happened. In an agent, the decision is the data. Without recording why the agent chose that path, you cannot debug, audit or improve.
Read the articleHow to choose the AI model for production (and why it is more than one)
The right question is not which model is best. It is which model for each task, with what fallback, tested against which cases, and switchable at what cost.
Read the articleHuman-in-the-loop: where the agent should stop and call a person
Human supervision is not a sign the agent failed. It is design. The mistake is treating it as a global switch: either everything goes through a person, or nothing does. Neither works.
Read the articleWhat MCP is, and why it became the standard for AI agents
The protocol solved the problem of connecting a model to a system. It does not solve identity, authorization or auditing, and that is where most projects stall.
Read the articleWhat it costs to put an AI agent into production
The license price is the smallest part of the bill. The seven components that form the real cost, and the calculation almost every company gets wrong between the pilot and production.
Read the articleMulti-agent architecture with a supervisor: how it works
An agent that tries to do everything gets worse as it grows. The supervisor pattern fixes that, and creates four new problems worth knowing about first.
Read the articleChatbot or AI agent: the difference, and when to use each
This is not a question of which technology is newer. It is a question of who decides the next step: whoever programmed the flow, or the system, in the moment.
Read the articleAI governance and the LGPD: what to require before production
An agent creates five points of exposure that an ordinary system does not have. What they are, what to require from the vendor, and what has to be ready before go-live.
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Bring a real process and see what changes.
In a no-cost conversation with an expert, we look at the process you have today and tell you plainly whether the case calls for an agent or whether the tool you already use solves it.