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