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agentic engineering / production ai agents that ship

agentic engineering.

we ship autonomous agents inside your product. state, memory, tool use, guardrails, evals. live in weeks, in your repo, on your own claude account.

we are the only studio in bangkok building production agent systems with senior engineers and nobody else. we built jobfin, the careers platform the belgian federal finance ministry hires through, still live since 2021. we have shipped for daikin, zalando, mediamarkt and board of innovation, and nemesis took awwwards site of the month. 60+ platforms since 2021. bangkok fintech ctos and saas founders get that exact team: multi-step agents, retrieval pipelines and ai-native product surfaces engineered into a next.js and supabase codebase you own from the first commit. no juniors. no handoffs. no no-code layer you rent forever.

lock your architecture scoping (usd 4,750 / thb 156,400)
the dlvx bangkok studio, sukhumvit

00 . the short answer

straight answers, updated 8 september 2026.

01

how much does it cost to build an ai agent in bangkok?

Architecture discovery is USD 4,750 (THB 156,400), a fixed fee credited to the build when you proceed. The build runs from USD 24,500 (THB 806,800) per phase, fixed price. Terms are 50/50 under USD 25k and 30/40/30 at USD 25k and above, paid by Stripe, Wise, or Thai bank transfer in THB. Every price is published on this page, which is more than any agency in Bangkok will show you before a call.

02

how long does it take to build an ai agent?

One to two weeks of discovery, then six to fourteen weeks of build. The written architecture spec and eval plan land before the first line of the agent loop is written. Build length tracks how many tools and integrations the agent touches. Launch comes with 90 days of care and two tuning passes against real production traffic.

03

do I need a prompt engineer or a software engineer to build an ai agent?

A software engineer, and a senior one. Prompt work gets a demo running once. Engineering gets it running the four hundredth time at 2am when the upstream API times out. The state machine, the typed and permissioned tool-use layer, retries with backoff and the eval suite in CI carry the same rigor as a payment flow. Our team is senior only: no juniors, no handoffs.

01 . what it is

the service in detail.

stack: next.js + supabase + claudepattern: state, memory, tool use, evalsownership: your repo, day oneteam: senior only, no juniors, no handoffs

a chatbot is a text box wired to an api key. an agent is a system. it holds state across steps, decides which tool to call and when, retries when a call fails, and stops itself before it does something it should not. we build the second one. an autonomous support agent that reads a ticket, checks three internal systems and drafts the reply your reviewer approves in ten seconds instead of ten minutes. a document pipeline that ingests thai and english invoices, extracts line items against your own catalog, and writes structured rows to postgres with a confidence score attached. a research agent that runs six searches, cross-checks the sources and returns a memo instead of a wall of links. that is what ships here, and it ships running.

read the full detail (4 more)

prompt engineering gets a demo to work once. software engineering gets it to work the four hundredth time, at 2am, when the upstream api times out and the agent retries with backoff instead of inventing an answer and sending it to your customer. that is why senior engineers own this work end to end and nobody junior touches it. the state machine that tracks where an agent sits inside a multi-step task. the tool-use layer that hands claude a typed, permissioned surface instead of raw api keys. the eval suite that catches a regression before your customers do. the guardrails that kill an unauthorized action before it executes. anthropic ships the model call. everything around that call, memory design, retry logic, cost control, evals, decides whether your agent runs for years or gets pulled in week two. we have been shipping production systems since 2021, 60+ of them, for daikin, zalando, mediamarkt and the belgian federal government. that is the engineering standing behind your agent.

it ships into your codebase, not ours. github repo under your org from the first commit, supabase project under your billing, claude usage on your own anthropic console. you own every line on day one and you keep shipping it even if you never speak to us again. already running an internal tool on next.js and supabase? we add the agentic layer straight into that codebase. auto-triage on the support queue, auto-draft on a document type, auto-reconcile on the finance export. no rebuild, no platform tax, no bolt-on tool wrapped around your product.

most 'ai automation' sold in bangkok is a zapier or make.com flow with a claude node stitched in. no state, no memory, no evals, no ownership, and no answer at all when the flow hits an edge case at 3am. it is a workflow toy wearing an agency invoice. an agent that reads customer data, drafts customer-facing replies or triggers a refund is a production surface, and it gets the same architecture rigor as a payment flow or an auth system. we run eu engineering standards out of brussels since 2021 and a bangkok studio on sukhumvit since 2024, so you get frankfurt-grade engineering with a desk you can walk into, pdpa and eu ai act ready on day one.

the engagement opens with a paid technical discovery, never a free strategy call. it produces a written architecture spec, state model, tool inventory, memory strategy and failure modes, plus the eval plan with the exact test cases and pass thresholds your build is measured against. all of it lands before one line of the agent loop is written. usd 4,750, fixed, credited to the build. the document is yours whether or not you build with us. every price sits published on this page, which is more than any agency in this city will put in writing before a call.

02 . who it is for

bangkok teams that get stuck here.

archetype 01

nok, a bangkok fintech cto shipping a support agent

series a lending platform, 45 employees, a support queue growing faster than headcount. the board wants an ai support agent live as a shipped product feature, not a chatbot glued to the marketing site. nok gets an agent that reads a ticket, pulls account status from postgres, checks three internal systems and drafts the reply a reviewer approves in ten seconds, with an eval suite that proves accuracy before launch and catches drift after. the same rigor we ship for daikin and the belgian federal government.

archetype 02

kade, a saas founder adding an agentic layer

internal ops tool already running on next.js and supabase, three years old, thousands of rows moved by hand every week. kade wants zero rebuild: auto-triage on the intake queue and auto-draft on the weekly report, shipped straight into the codebase that already exists. the agent respects the rls policies and the audit log already in place. we work inside your repo, so nothing gets thrown away and nothing gets rented back to you.

archetype 03

aew, ops director at a bangkok logistics or dtc seller

multi-warehouse fulfilment across three marketplaces, shopee, lazada and a shopify own-site, with reconciliation done by two people in a spreadsheet every friday. aew gets a multi-step agent wired into the existing ops app: read the marketplace order feed, cross-check stock across warehouses, flag mismatches, draft the reconciliation entry, and escalate to a human the second a number looks wrong. friday afternoon goes back to the team.

03 . what you receive

concrete delivery, no abstractions.

04 . timeline

the studio method, four phases.

phase 0

discover and architect.

1 to 2 weeks

paid technical discovery. stakeholder interviews, a full review of your existing codebase, state model design, tool inventory, memory strategy and failure-mode mapping. you walk out with the architecture spec and the eval plan, signed before a build day starts.

phase 1

build the core agent loop.

weeks 1 to 3 of build

the state machine, the model-calling layer, the first tool integrations wired. weekly demo with the senior engineers who wrote the code, never an account manager. by the end of week 3 your agent completes a multi-step task end to end.

phase 2

integrate tools and guardrails.

weeks 4 to 6 of build

the full tool inventory wired, internal apis, database reads and writes, external services, with permission boundaries enforced per tool, retry and backoff logic, and the guardrails that kill an unauthorized action before it executes.

phase 3

eval, harden, and ship.

weeks 7 to 9, plus 90 days

the eval suite runs against the full test set, observability goes live, production cutover, handover documentation delivered. then 90 days of post-launch care, tuning against real traffic instead of synthetic test cases.

05 . pricing

transparent. no hidden lines.

price by scopefixed per phase

paid technical discovery: usd 4,750 (thb 156,400), fixed fee, credited to the build when you proceed. it covers stakeholder interviews, a full codebase review, the architecture spec and the eval plan, and you keep the document either way. build runs from usd 24,500 (thb 806,800) per phase, fixed price, the same discipline as our custom apps builds: 6 to 14 weeks end to end depending on how many tools and integrations your agent touches. thb shown at 32.93 to the dollar, bot reference rate at invoice date is what bills. usd or thb both accepted. terms: 50/50 for engagements under usd 25k, 30/40/30 for engagements usd 25k and above. stripe usd, wise usd, or thai bank transfer in thb. every number is on this page before you ever speak to us.

06 . proof

cases that carry this service.

07 . questions

what bangkok buyers ask first.

01

how is this different from just using chatgpt or claude directly?

chatgpt or claude in a browser tab is a person typing a prompt and reading an answer. what we build is a system: the agent holds state across a multi-step task, calls your internal tools and apis through a permissioned layer, retries when a call fails, and passes an eval suite before and after it ships. the model call is the smallest part of the work. the state machine, the guardrails, the observability and the retrieval layer are the engineering, and none of them exist when someone opens a chat window.

02

how do you prevent an agent from doing something wrong?

guardrails are designed during discovery, never bolted on after an incident. every tool the agent can call is permissioned and scoped: an agent that drafts a refund cannot execute one without a human approval step unless the architecture spec authorizes exactly that. retries have backoff and a hard stop. every run is logged, so an anomaly surfaces in minutes instead of arriving as a customer complaint. the eval suite runs the agent against known edge cases before every deploy, failure cases included.

03

do we own the code?

yes, and completely. github repo under your org from the first commit, supabase project under your billing, claude usage on your own anthropic console. the agent ships inside your codebase, never a tool you rent from us. if we vanished tomorrow, your engineers keep shipping it on monday.

04

can this run alongside our existing ai-integration or claudekick setup?

yes, they stack. claudekick automates operational workflows on top of the tools you already run: your accounting software, your line oa, your crm. agentic engineering ships agent capability inside the product itself, a feature your customers and your team touch every day. run claudekick for the finance team's monthly close and a support agent from this service inside the product, both on one claude account, zero conflict.

05

what does 'eval suite' mean in practice?

a documented set of test cases your agent passes before it ships and before every future deploy: known inputs, expected behavior, pass or fail thresholds, edge cases and failure modes included. it is wired into ci, so a prompt change, a model version upgrade or a new tool integration gets checked automatically instead of waiting for someone to notice the agent got worse. this is the line between an agent you trust in production and a demo that worked once.

06

what stack do you use?

next.js and supabase as the application layer, the same stack as our custom apps builds. claude for the model layer, on anthropic's tool-use apis for the agent's actions. postgres with pgvector or a dedicated vector store when retrieval is part of the design. sentry and supabase logs for observability, ci-wired eval runs on every deploy. aws in the region you choose, or fly.io when latency or data residency decides it.

07

how much does it cost to build an ai agent in bangkok?

Architecture discovery is USD 4,750 (THB 156,400), a fixed fee credited to the build when you proceed. The build runs from USD 24,500 (THB 806,800) per phase, fixed price. Terms are 50/50 under USD 25k and 30/40/30 at USD 25k and above, paid by Stripe, Wise, or Thai bank transfer in THB. Every price is published on this page, which is more than any agency in Bangkok will show you before a call.

08

how long does it take to build an ai agent?

One to two weeks of discovery, then six to fourteen weeks of build. The written architecture spec and eval plan land before the first line of the agent loop is written. Build length tracks how many tools and integrations the agent touches. Launch comes with 90 days of care and two tuning passes against real production traffic.

09

do I need a prompt engineer or a software engineer to build an ai agent?

A software engineer, and a senior one. Prompt work gets a demo running once. Engineering gets it running the four hundredth time at 2am when the upstream API times out. The state machine, the typed and permissioned tool-use layer, retries with backoff and the eval suite in CI carry the same rigor as a payment flow. Our team is senior only: no juniors, no handoffs.

10

what do you get when you pay someone to build an ai agent?

An architecture spec and eval plan, the agent implementation with typed tool use and guardrails, a retrieval and memory layer, observability on every agent run, a documented eval suite wired into CI, a 20-page operating manual, and 90 days of post-launch tuning. All of it inside your own Next.js and Supabase codebase, owned by you from the first commit.

11

is an ai agent better than a zapier or make automation?

It depends who touches it. A Zapier or Make flow with a Claude node has no state, no memory, no eval suite, and no answer for an edge case at 3am. That is fine for one-off internal automation, which DLVX covers under AI integration. An agent is for the feature paying customers and staff use inside your product every day.

12

how is an ai agent different from just using chatgpt or claude?

A chat window is a person typing a prompt and reading an answer. An agent holds state across a multi-step task, calls your internal tools and APIs through a permissioned layer, retries when a call fails, and passes an eval suite before and after every ship. The model call is the smallest part of the engineering.

13

do I own the code if dlvx builds my ai agent?

Yes. The GitHub repo sits under your org from the first commit, the Supabase project is under your billing, and Claude usage runs on your own Anthropic console. The agent ships inside your codebase, never a tool you rent from DLVX, so if DLVX vanished tomorrow your engineers keep shipping it on Monday.

14

how do you stop an ai agent from doing something wrong?

Guardrails are designed during discovery, never bolted on after an incident. Every tool the agent can call is scoped and permissioned, so an agent that drafts a refund cannot execute one without human approval unless the spec authorizes it. Retries have a hard stop, every run is logged, and the eval suite runs before every deploy.

07b . คำถามที่ถามบ่อย

คำถามที่ธุรกิจในกรุงเทพถามก่อนตัดสินใจ

อ่านคำถามและคำตอบภาษาไทยทั้งหมด (5)
01

จ้างทำ AI agent ราคาเท่าไหร่?

เริ่มที่ USD 4,750 (THB 156,400) สำหรับเฟสออกแบบสถาปัตยกรรม เป็นราคาเหมาจ่าย และหักเป็นเครดิตให้เมื่อทำต่อถึงขั้นพัฒนาจริง ส่วนงานพัฒนาเริ่มที่ USD 24,500 (THB 806,800) ต่อเฟส ราคาคงที่ แบ่งจ่าย 50/50 ถ้างานต่ำกว่า USD 25,000 และ 30/40/30 ถ้าตั้งแต่ USD 25,000 ขึ้นไป ชำระผ่าน Stripe, Wise หรือโอนเข้าบัญชีธนาคารไทยเป็นเงินบาทได้ ทุกราคาแสดงไว้บนหน้านี้หมดแล้ว ก่อนคุยกันด้วยซ้ำ

02

AI agent ต่างจาก chatbot ทั่วไปยังไง?

chatbot คือกล่องแชทที่ต่อ API ไว้เฉยๆ ส่วน agent เป็นระบบที่เก็บสถานะงานข้ามหลายขั้นตอน เลือกเรียกเครื่องมือภายในผ่านชั้นที่กำหนดสิทธิ์ไว้ชัดเจน ลองใหม่อัตโนมัติเมื่อเรียกไม่สำเร็จ และต้องผ่านชุดทดสอบ eval ก่อนขึ้นระบบจริงทุกครั้ง งานส่วนใหญ่จึงเป็นงานวิศวกรรมซอฟต์แวร์ระดับเดียวกับระบบชำระเงิน ไม่ใช่แค่การเขียน prompt

03

ทำ AI agent ใช้เวลากี่เดือน?

เฟสออกแบบสถาปัตยกรรมใช้เวลา 1-2 สัปดาห์ ได้เอกสาร architecture spec และแผน eval ก่อนเริ่มเขียนโค้ดจริง จากนั้นงานพัฒนาใช้เวลา 6-14 สัปดาห์ ขึ้นอยู่กับว่า agent ต้องเชื่อมต่อกับระบบและเครื่องมือภายในกี่ตัว หลังขึ้นระบบยังดูแลต่ออีก 90 วัน พร้อมปรับจูน 2 รอบจากการใช้งานจริง ไม่ใช่จากเคสทดสอบ

04

โค้ดที่จ้างทำ AI agent เป็นของใคร?

เป็นของคุณทั้งหมดตั้งแต่วันแรก โค้ดอยู่ใน GitHub repo ขององค์กรคุณตั้งแต่ commit แรก ฐานข้อมูลอยู่ใน Supabase project ที่จ่ายค่าบริการในนามบริษัทคุณเอง และการเรียกใช้ Claude ผูกกับบัญชี Anthropic ของคุณ agent ติดตั้งอยู่ในโค้ดเบสของคุณ ไม่ใช่เครื่องมือที่ต้องเช่าจาก DLVX ถ้าวันหนึ่งไม่ได้ทำงานกันต่อ ทีมพัฒนาของคุณรันเองได้ทันที

05

ธุรกิจแบบไหนควรลงทุนทำ AI agent จริงจัง?

เหมาะกับ CTO สาย fintech หรือ SaaS และผู้ก่อตั้ง scale-up ในกรุงเทพฯ ที่ต้องการให้ agent เป็นฟีเจอร์จริงในโปรดักต์ ให้ลูกค้าหรือทีมงานใช้ทุกวัน ไม่ใช่แค่ทำ demo ให้ผู้บริหารดู ทีมเดียวกันนี้สร้างแพลตฟอร์มรับสมัครงานให้กระทรวงการคลังของรัฐบาลเบลเยียม และทำงานให้ Daikin, Zalando, MediaMarkt มาแล้ว ถ้าต้องการแค่ระบบอัตโนมัติภายใน เช่น ต่อกับ LINE OA, FlowAccount, PEAK หรือ Xero บริการ AI integration ของ DLVX ราคาถูกกว่าและตอบโจทย์ตรงกว่า

08 . further

not quite the right fit?

ready to scope the architecture?

agentic engineering starts with a written spec.

the paid technical discovery hands you the architecture spec and the eval plan before a line of the agent loop gets written. fixed fee, credited to the build, yours to keep either way. same studio that took propick property's google clicks from 39 to 79 and impressions from 1,163 to 3,955 in 28 days. brussels since 2021, sukhumvit since 2024, line official account open for thai-first contact.