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AI agents are systems that plan, call tools, and act rather than just answer. The engineering challenge is not getting one to work in a demo but making it behave predictably, stay within budget, and fail safely when a model does something unexpected.
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Pre-vetted engineers ready to join your Slack and GitHub within 72 hours of agreement.
Mandatory 4–5 hour daily overlap with your EST/PST or GMT team for real-time collaboration.
You own every line of code from day one. Strict NDAs signed before any discussion begins.
Daily standups, weekly demos, and full Jira/GitHub visibility. No surprises, ever.
Every developer has 3+ years of production experience. We do not staff junior engineers on client projects.
Scale your team up or down on a monthly basis - no long-term contracts or termination penalties.
Agent demos are easy. An agent that runs unattended against real customer data for six months is a different engineering problem, and most of the difficulty sits outside the model.
The questions that decide success are mundane: what happens when a tool call fails, how do you stop an agent looping, what does it do when it is uncertain, and how would you reconstruct why it took an action three weeks ago. Teams that skip these ship something impressive that quietly becomes untrustworthy.
We design the failure behaviour first. An agent that stops and asks a human when confidence is low is far more valuable than one that always produces an answer, because people will actually rely on it.
The most important safety mechanism is scoping tools narrowly. An agent with database write access and a vague instruction is a bad idea regardless of how good the model is.
We give agents the smallest set of capabilities that accomplishes the task, with consequential actions — sending messages, moving money, modifying records — routed through explicit confirmation rather than executed autonomously. That boundary is a product decision as much as a technical one, and it should be deliberate.
Everything an agent does gets logged with its reasoning and inputs. When someone asks why the system did something, you need an answer better than a shrug.
Without an evaluation set, improving an agent is guesswork. You change a prompt, it seems better on the three examples you tried, and you have no idea whether you regressed something else.
We build a fixed set of representative cases with expected outcomes before building the agent, and run it on every change. This is the single practice that most separates teams who improve their AI systems steadily from teams who plateau and start arguing about model choice.
It also gives you a defensible answer when someone asks whether the system is getting better, which is a question that arrives eventually and deserves data rather than impressions.
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