Agent Build

A delivered, working AI workflow running reliably in your ecosystem. I take a scoped opportunity from discovery through deployment and stabilization — whether the right shape turns out to be a custom-built agent, an off-the-shelf vendor product configured into your stack, or a hybrid. Four to twelve weeks. You get a running workflow, the documentation to operate it, and a 30-day stabilization window. The implementation shape is locked at the end of discovery; the proposal commits to the outcome, not the means.

What you get

• A working AI workflow running in the agreed environment — the deliverable is the workflow, regardless of whether it’s custom-built, vendor-integrated, or hybrid.
• Implementation assets in a git repository you own with full transfer rights — source code where I wrote it, vendor configuration exports and setup docs where I configured a vendor tool, IaC for any infrastructure I set up.
• Architecture documentation — system diagram (Mermaid), data flow, tool and vendor surface, decision logic. Covers the chosen implementation shape end to end.
• Runbook — how to operate, monitor, and respond to common incidents. PDF + Markdown source.
• Test suite — automated tests covering the workflow’s core behaviors and key failure modes; end-to-end smoke tests against vendor APIs where applicable.
• Logging & monitoring — structured logs and dashboards sized to the agreed scope.
• 30-day post-go-live coverage — bug fixes, threshold tuning, vendor-configuration adjustments, on-call coverage at the agreed SLA.
• Demo recordings — recorded video walkthroughs across the engagement so stakeholders who can’t attend live watch on their own time.

How it works

Four phases. Specific week-by-week milestones are derived from the discovery-locked scope and recorded in your timeline at engagement start. Sync cadence and demo count are set at the same time. Async updates land daily in a shared channel during active weeks.

Who it’s for

• You’ve identified the workflow you want automated and you have decision authority to ship it (CTO, VP Engineering, sometimes CEO at smaller companies).
• You’re open to whatever implementation shape fits — custom code, vendor configuration, or hybrid — and would rather have one accountable partner deliver the working workflow than manage that choice yourself.
• For custom-built components: you can provision a staging environment and grant API access during the engagement.
• Your budget for the build is $18k–$45k and you can commit to the 40/40/20 payment schedule.
• You want a single accountable engineer, not a team of contractors managed through a project manager.

Not the right fit if

• You haven’t decided what AI workflow you need — start with an Audit first; it will recommend custom, vendor, or hybrid.
• You want fully autonomous AI with no maintenance — no honest vendor offers this, regardless of who built it.
• You want reflecttech to write custom code for something better solved by configuring a vendor product. I’ll tell you that during discovery and recommend the vendor route, not pad the engagement.

What it costs

Tier-banded dollar amount, locked at engagement start based on the discovery-locked scope. The marketing page shows the band; the exact number inside the band is set in your proposal.

Payment schedule (binding): 40% upfront at engagement signature (before kickoff), 40% at midpoint delivery (set at scope lock), 20% at go-live (workflow running in production, runbook delivered, handoff complete). All payments via Stripe, Net-0.

What’s not included: LLM API costs and vendor subscription fees (billed to your accounts; I set up cost monitoring during discovery), infrastructure costs (your cloud bill), post-stabilization changes outside the agreed scope (handled via change order or rolled into a Partner Retainer), custom dashboards beyond the monitoring sized to the agreed scope.

What happens next