Custom AI Agent Development
Custom AI Agent Development is an engineering engagement that builds an AI agent to execute a real business process: reading data, making decisions, and acting through APIs, with guardrails and human checkpoints. It is for repetitive multi-step work that consumes skilled hours but follows learnable rules.
The problem this solves
Somebody on your team spends hours a week on the same multi-step routine: gather data from three systems, apply judgment that mostly follows rules, write the result somewhere, notify someone. It is too variable for classic automation, too repetitive to justify the salary hours, and it silently caps how much your operation can handle without hiring.
How we work
We pick the process deliberately: clear inputs, a describable decision policy, measurable output, and tolerable cost of error. Not every process qualifies, and telling you which ones do not is part of the work.
Then we build the agent: a defined toolset of API actions it may take, an LLM reasoning core, and guardrails that match the stakes, scoped permissions, spending and volume limits, approval gates on irreversible steps, and full logging of every decision and action. We develop in Python and Node against the APIs involved, HubSpot's included, and test the agent on historical cases before it touches live data. We run agents in our own daily operations, content, research, ops routines, so the failure modes we engineer against are ones we have personally met.
Deployment is staged: shadow mode first where output is reviewed, then supervised autonomy, then the steady state your risk tolerance supports, with monitoring throughout.
Deliverables
- Process selection and feasibility assessment
- Agent with defined toolset, permissions, and reasoning policy
- Guardrails: approval gates, limits, full action logging
- Test report against historical cases
- Staged rollout: shadow mode to supervised to steady state
- Monitoring, alerting, and an operations runbook
What buyers ask before scoping.
Which processes are actually a good fit for an AI agent?
Ones with clear inputs, rules a competent employee could write down, and errors that are cheap to catch: data enrichment and triage, research and summarization, drafting that a human approves, cross-system housekeeping. Poor fits: high-stakes irreversible decisions and processes your own team cannot describe consistently. We assess this honestly before building.
How do you prevent the agent from doing something it should not?
Structurally, not hopefully: the agent can only call the tools we give it, with the narrowest API permissions that work, hard limits on volume and spend, and mandatory human approval on irreversible actions. Every step is logged and reviewable. The design assumption is that the model will occasionally be wrong, and the system contains it.
Who maintains the agent after handover?
Your choice. We hand over with a runbook, monitoring, and documented architecture, so a technical team can own it. Most clients keep us on a maintenance arrangement instead, because models, APIs, and the process itself evolve, and periodic evaluation against fresh cases is what keeps output quality honest.
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Sounds like your situation?
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