Demos are easy. Agents you can trust.
Production AI agents for real business operations: grounded in your data, guarded by bounded tools and human gates, graded against a written standard — before deployment and after. Integrated with the stack you already run.
100% in-house development · Oregon & California, USA · 25+ years building production systems
The agent contract — illustrationConsumer guide: AI attendants
The short answer
Updated July 2026
Houtos Labs builds AI agents that hold up in production. Internal ops agents, customer-facing assistants, and multi-agent workflows — grounded in your data with citations, guarded by bounded tools and human approval gates, and graded by an evaluation suite before and after deployment. Integrated with the tools you already run, not another app to remember.
The rule that keeps agents honest is the same one that runs this studio: an estimate is never dressed as a measurement, and an agent action that matters gets a human gate. (Plain-language primer: should your business have an AI attendant?) Store-side automation lives in our e-commerce practice.
Talk to a sales agent that never sleeps
A live-chat sales agent for an imaginary appliance repair company — grounded in its service catalog, honest about prices, and gated where money moves. Pick a reply and watch how it works the lead.
The first five minutes decide the lead
Most repair shops answer after the customer has already called a competitor. The agent answers in seconds, quotes only from the real price book, and books real calendar slots — with a human gate on anything unusual.
- Grounded — answers cite the service catalog and route map
- Gated — discounts and exceptions escalate to a human
- Logged — the full transcript lands on the tech's job board
Scripted demonstration with a fictional company — a live deployment answers from your catalog, prices, and calendar.
My washer is leaking from the bottom mid-cycle.
2:14 PMSorry about that — a mid-cycle leak from the bottom is usually a door seal or the drain pump, and both are same-visit fixes. I can have a tech at your place tomorrow between 9–11 AM. Want me to hold that slot?
Summit Assistant · 2:14 PM · sourced from the service catalogHow much will it cost?
2:15 PMFair question. The visit + diagnosis is $89, credited toward the repair. A door seal runs $140–$190 installed; a drain pump $180–$240. The exact number is confirmed on-site before any work starts — no surprises.
Summit Assistant · 2:15 PM · price book v2026-06Book it
2:15 PMDone — tomorrow, 9–11 AM is held for you. You'll get a text confirmation and a 30-minute heads-up when the tech is on the way. 👍
Summit Assistant · 2:16 PM · calendar + job board updatedScripted demonstration · fictional company · no data leaves this page — a live deployment answers from your catalog, prices, and calendar.
The revenue loop, end to end
One agent system carrying a customer from first contact to repeat business — the workflow we design and build, with the human placed exactly where judgment matters.
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01
Lead intake
Form, chat, phone, and socials land in one queue — nothing waits in a voicemail box.
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02
Profile build
The agent assembles a customer picture: location, property type, urgency, likely budget register.
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03
Tailored first response
Written in the customer's register, from the real catalog. Design target: under 5 minutes.
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04
Sales handoff
Your rep gets the profile plus a suggested approach — pace, proof points, what this customer type responds to.
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05
Funnel management
The agent project-manages the deal for the rep: reminders, next steps, stalled-deal flags.
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06
Close
The human closes; the agent preps documents, scheduling, and the welcome sequence.
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07
Review capture
A timed, personal review ask when satisfaction peaks — routed to the platforms that matter.
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08
Care loop ↺
Scheduled check-ins, service reminders, and honest upsells — recurring revenue and referrals, back to 01.
Amber nodes = human gates. "Under 5 minutes" is the design target we build against, not a universal guarantee — your volumes set the final SLA in the scope.
An agent run, traced
This is what "production-grade" actually looks like: not magic — a disciplined loop with receipts. Pick a task and watch the trace. Scripted simulation; the discipline is the real product.
Same loop every run:
ground → plan → act →
verify → hand off
- groundread ledger — 4 invoices >30 days overdue, histories attached
- plantone per customer history: 3 gentle · 1 firm
- act4 reminder drafts written, amounts reconciled to the ledger
- verifyamounts re-checked against source · 4/4 match
- gatehuman approves before anything sends — the agent never mails money conversations alone
- donequeued for approval · full trace logged
Scripted demonstration — the loop, gates, and logging shown are the real architecture we ship.
Evaluation discipline: how we grade.
What our agent development covers
From one well-guarded assistant to an orchestrated team of agents — scoped in writing, gated where the risk lives.
Internal ops agents
Invoice chasing, record reconciliation, report compilation, inbox triage — the repetitive work that eats your team's week, done with receipts and human gates on anything consequential.
Customer-facing assistants
Grounded in your real docs and policies, honest about what they don't know, and designed to hand off to humans gracefully — an attendant that helps customers, not a chatbot that embarrasses you.
Multi-agent systems & orchestration
Specialist agents coordinated through shared state, verification lanes, and clear ownership — the architecture patterns we run in our own operations, applied to yours.
Evaluation & guardrails
The part that makes it production-grade: eval suites against known cases, bounded tool permissions, action logging, and monitoring after deploy. If it can't be graded, it doesn't ship — the studio rule, applied to agents.
Ground. Guard. Grade.
Cited answers · bounded tools · human gates where the risk lives
Proof, honestly
The standing rule: the trace above says "scripted" on its face because it is — what's real is the architecture it shows, and the fact that this studio runs on the same discipline it sells: multi-agent workflows, verification lanes, and written standards, every working day. We build agents the way we use them.
Work, labeled as it is
Agent and product systems with real statuses — live, in build, or concept.
The MethodThe rubric, published
The written-standard discipline agents are evaluated against.
The Owner's ManualThe plain-language primer
What an AI attendant can really do — the honest consumer guide.
The Design StudioThe page that proves the craft
Live exhibits and the Measurement Engine — operable right now.
Fair questions
What is business AI agent development?
Building AI systems that do real work inside real operations: reading the tools you already run (email, CRM, tickets, spreadsheets, databases), reasoning over a task, taking bounded actions, and — the part most vendors skip — being evaluated against a written standard before and after deployment. An agent without guardrails and evaluation isn't automation; it's a liability with an API key.
What can AI agents actually do reliably in 2026?
Reliably: retrieve and synthesize from your own data, draft for human review, triage and route, reconcile records across systems, monitor and flag, and run multi-step workflows with checkpoints. Unreliably (still): fully autonomous high-stakes actions without human gates. Honest agent design puts the human approval exactly where the risk lives — we'll tell you which side of that line each use case sits on.
How do you keep an AI agent from making things up?
Grounding, guarding, and grading. Grounding: the agent answers from your documents and systems, with citations back to the source. Guarding: bounded tools, deterministic rules for anything consequential, human approval gates on risky actions. Grading: an evaluation suite that tests the agent against known cases before deployment and monitors it after — the same measure-then-ship discipline as everything else we build.
Can agents integrate with the tools we already use?
That's the whole point. Agents earn their keep inside your existing stack — email, Slack, CRMs, ticketing, accounting, custom databases and APIs — not in a separate app your team has to remember to open. Integration scope is defined in writing: which systems, which permissions, which actions need a human, and what gets logged.
Automation you can audit.
Tell us which hours your team keeps losing — the reply is a reading of what an agent can honestly take off their plate, and what it shouldn't.
100% in-house development · Oregon & California, USA · 25+ years building production systems
grounded · guarded · graded · human gates where the risk lives — privacy