[{"data":1,"prerenderedAt":653},["ShallowReactive",2],{"site-schema":3,"article-en-choose-ai-agent-company-2026":93,"article-related-en-choose-ai-agent-company-2026":635},{"@context":4,"@graph":5},"https://schema.org",[6,72,84],{"@type":7,"@id":8,"name":9,"alternateName":10,"legalName":15,"foundingDate":16,"numberOfEmployees":17,"taxID":18,"url":19,"logo":20,"founder":21,"contactPoint":23,"address":37,"location":43,"sameAs":63},"Organization","https://joinx.co/#organization","JoinX哲煜科技",[11,12,13,14],"JoinX","TWJOIN","哲煜科技","哲煜科技股份有限公司","JoinX Co., Ltd.","2016-04-28",80,"43774383","https://joinx.co","https://joinx.co/images/logo-with-name.png",{"@id":22},"https://joinx.co/#person-jack-lee",[24],{"@type":25,"contactType":26,"url":27,"telephone":28,"email":29,"areaServed":30,"availableLanguage":33},"ContactPoint","customer service","https://joinx.co/contact-us","+886-2-8771-9095","service@joinx.co",[31,32],"TW","JP",[34,35,36],"zh-Hant","en","ja",{"@type":38,"streetAddress":39,"postalCode":40,"addressLocality":41,"addressRegion":42,"addressCountry":31},"PostalAddress","民生東路二段170號8樓","104","台北市中山區","台灣",[44,51,57],{"@type":45,"name":46,"address":47},"Place","JoinX 台中辦公室",{"@type":38,"streetAddress":48,"postalCode":49,"addressLocality":50,"addressRegion":42,"addressCountry":31},"台灣大道二段360號21樓C室","40453","台中市北區",{"@type":45,"name":52,"address":53},"JoinX 高雄辦公室",{"@type":38,"streetAddress":54,"postalCode":55,"addressLocality":56,"addressRegion":42,"addressCountry":31},"民族一路80號2樓之一(B4)","807","高雄市三民區",{"@type":45,"name":58,"address":59},"JoinX 東京辦公室",{"@type":38,"streetAddress":60,"postalCode":61,"addressLocality":62,"addressCountry":32},"東五反田5-22-37 Office Circle N 五反田 9樓","141-0022","東京都品川區",[64,65,66,67,68,69,70,71],"https://www.crunchbase.com/organization/twjoin","https://www.facebook.com/JoinX.TW","https://tw.linkedin.com/company/%E5%93%B2%E7%85%9C%E7%A7%91%E6%8A%80","https://www.youtube.com/c/joinstuido%E5%93%B2%E7%85%9C%E7%A7%91%E6%8A%80","https://medium.com/twjoin","https://www.104.com.tw/company/1a2x6bjomb","https://clutch.co/profile/twjoin","https://www.goodfirms.co/company/twjoin",{"@type":73,"@id":22,"name":74,"alternateName":75,"jobTitle":76,"worksFor":77,"url":78,"sameAs":79},"Person","李秉哲","Jack Lee","創辦人暨執行長",{"@id":8},"https://jack.joinx.co",[80,81,82,83],"https://www.instagram.com/jack88.podcast/","https://www.threads.net/@jack88.podcast","https://joinx.co/jack88/","https://www.linkedin.com/in/jacklee-taiwan/",{"@type":85,"@id":86,"url":19,"name":9,"alternateName":87,"publisher":88,"inLanguage":89},"WebSite","https://joinx.co/#website",[11,13,9],{"@id":8},[90,91,92],"zh-Hant-TW","en-US","ja-JP",{"id":94,"title":95,"body":96,"category":596,"cover":597,"dateModified":598,"description":599,"extension":600,"faq":601,"hasCoverTitle":625,"locale":35,"meta":626,"navigation":625,"need":627,"path":628,"seo":629,"sources":630,"stem":631,"takeaways":630,"time":632,"type":633,"__hash__":634},"content/en/article/choose-ai-agent-company-2026.md","Choosing an AI Agent Developer: 2026 Costs, Platform vs Custom",{"type":97,"value":98,"toc":560},"minimal",[99,106,109,123,128,131,231,234,238,241,244,321,328,332,335,363,366,370,373,378,381,385,388,392,395,399,402,406,409,413,417,420,424,427,431,434,438,441,462,466,469,472,476,480,483,487,490,494,497,501,504,508,511,515,518,522,525,529,532,536,539,543,546,550,553,557],[100,101,102],"p",{},[103,104,105],"strong",{},"When choosing an AI agent developer, what you really need to compare is not who gives the best model demo, but who can connect agents safely to your business processes so that every action is controlled, traceable, and recoverable.",[100,107,108],{},"AI agents can understand tasks, plan steps, call tools, and write results back into systems. This makes them closer to a \"digital worker\" than an ordinary chatbot, and it also means the risks of adoption extend from answer quality to permissions, data, and operational responsibility.",[100,110,111,112,117,118,122],{},"If you are still working out the differences between AI, generative AI, RAG, and agents, start with our ",[113,114,116],"a",{"href":115},"/en/article/2026-enterprise-ai-gen-ai-rag-ai-agent/","guide to enterprise AI technologies",". If you want to set out an overall adoption path first, see our ",[113,119,121],{"href":120},"/en/article/2026-ai-adoption-service-guide/","guide to enterprise AI adoption services",".",[124,125,127],"h2",{"id":126},"platform-subscription-or-custom-development-how-do-you-choose","Platform subscription or custom development: how do you choose?",[100,129,130],{},"JoinX recommends first looking at whether the agent needs access to company data, has to carry out actions across systems, and will be responsible for part of a production process. Standard needs can be validated on a platform first, while heavy integration or strict governance requirements are better suited to custom development.",[132,133,134,150],"table",{},[135,136,137],"thead",{},[138,139,140,144,147],"tr",{},[141,142,143],"th",{},"Comparison",[141,145,146],{},"AI agent platform subscription",[141,148,149],{},"Custom AI agent development",[151,152,153,165,176,187,198,209,220],"tbody",{},[138,154,155,159,162],{},[156,157,158],"td",{},"Speed to launch",[156,160,161],{},"Standard scenarios can be trialled quickly",[156,163,164],{},"Requirements, integration, and acceptance must be completed",[138,166,167,170,173],{},[156,168,169],{},"Upfront cost",[156,171,172],{},"Lower, billed per seat or by usage",[156,174,175],{},"Higher, including engineering and governance design",[138,177,178,181,184],{},[156,179,180],{},"Process flexibility",[156,182,183],{},"Limited by the platform's nodes and connectors",[156,185,186],{},"Can be designed around company rules and exception flows",[138,188,189,192,195],{},[156,190,191],{},"Permission governance",[156,193,194],{},"Depends on the platform's existing capabilities",[156,196,197],{},"Can enforce internal roles, approvals, and auditing",[138,199,200,203,206],{},[156,201,202],{},"System integration",[156,204,205],{},"Easier with common SaaS",[156,207,208],{},"Suited to ERP, CRM, legacy systems, and private APIs",[138,210,211,214,217],{},[156,212,213],{},"Model choice",[156,215,216],{},"Depends on the platform's supported list",[156,218,219],{},"Can be designed for multiple models and swapping",[138,221,222,225,228],{},[156,223,224],{},"Long-term responsibility",[156,226,227],{},"Shared between the platform and the company",[156,229,230],{},"Must be clearly defined between the developer and the company",[100,232,233],{},"When the process is simple, the data is not very sensitive, and you only want to test whether people will use it, starting with a platform is usually reasonable. If the agent will look up customer data, create orders, update inventory, or trigger payments on behalf of employees, you cannot judge it only by how easy the interface is; it has to be planned as a production enterprise system.",[124,235,237],{"id":236},"how-much-does-ai-agent-development-cost-in-2026","How much does AI agent development cost in 2026?",[100,239,240],{},"The cost of developing an AI agent depends on the task scope, data sources, system integrations, and governance requirements. Define tasks that can be accepted before asking for quotes, or the prices cannot be compared directly.",[100,242,243],{},"The ranges below are typical for custom projects in Taiwan. They are a starting point for budget planning, not fixed quotes.",[132,245,246,263],{},[135,247,248],{},[138,249,250,253,257,260],{},[141,251,252],{},"Phase",[141,254,256],{"align":255},"right","Estimated cost",[141,258,259],{},"Typical scope",[141,261,262],{},"Indicative timeline",[151,264,265,279,293,307],{},[138,266,267,270,273,276],{},[156,268,269],{},"Use case assessment and technical validation",[156,271,272],{"align":255},"NT$100K–300K",[156,274,275],{},"Process review, data sampling, risk and architecture recommendations",[156,277,278],{},"2–4 weeks",[138,280,281,284,287,290],{},[156,282,283],{},"Single-use-case PoC",[156,285,286],{"align":255},"NT$300K–800K",[156,288,289],{},"One task, limited data, human supervision",[156,291,292],{},"4–8 weeks",[138,294,295,298,301,304],{},[156,296,297],{},"Single production agent",[156,299,300],{"align":255},"NT$800K–2.5M",[156,302,303],{},"Permissions, integration, monitoring, testing, and deployment",[156,305,306],{},"2–4 months",[138,308,309,312,315,318],{},[156,310,311],{},"Multi-agent enterprise system",[156,313,314],{"align":255},"NT$2.5M and up",[156,316,317],{},"Cross-department, multiple systems, orchestration, and full governance",[156,319,320],{},"4 months and up",[100,322,323,324,122],{},"What drives the cost most is not \"which model is used\", but how much the agent can do, how many systems it connects to, whether the data has been prepared, and how much damage an error could cause. For a fuller look at AI adoption budgets and how to compare vendors, read on in our ",[113,325,327],{"href":326},"/en/article/choose-ai-adoption-company-2026/","2026 guide to choosing an AI adoption company",[124,329,331],{"id":330},"what-ongoing-costs-does-an-ai-agent-have-after-launch","What ongoing costs does an AI agent have after launch?",[100,333,334],{},"After launch there are usually still costs for model usage, data maintenance, monitoring and governance, and system operations. Assess options by total cost of ownership rather than comparing only the initial development quote.",[336,337,338,345,351,357],"ol",{},[339,340,341,344],"li",{},[103,342,343],{},"Model usage:"," token or inference fees for text, images, voice, and long context.",[339,346,347,350],{},[103,348,349],{},"Data and tool APIs:"," search, OCR, maps, messaging, vector databases, and third-party system fees.",[339,352,353,356],{},[103,354,355],{},"Monitoring and security:"," log retention, alerting, vulnerability management, and permission and audit tools.",[339,358,359,362],{},[103,360,361],{},"Operations and improvement:"," knowledge updates, prompt tuning, test set maintenance, model upgrades, and incident handling.",[100,364,365],{},"When comparing prices, ask vendors to list the build cost, estimated monthly fees, usage assumptions, and overage pricing together, rather than looking only at the one-off development amount.",[124,367,369],{"id":368},"how-can-you-tell-whether-an-ai-agent-developer-can-really-integrate","How can you tell whether an AI agent developer can really integrate?",[100,371,372],{},"A suitable developer must be able to handle data, permissions, existing systems, acceptance, and operations. The ability to demo a model is usually not enough to support a production workflow.",[374,375,377],"h3",{"id":376},"_1-can-they-handle-enterprise-system-integration","1. Can they handle enterprise system integration?",[100,379,380],{},"The vendor should be able to explain what they do when APIs are incomplete, legacy systems have no documentation, data formats are inconsistent, or the work spans network segments. Real agent engineering is often not about \"connecting a model\" but about making tools run reliably in the existing environment.",[374,382,384],{"id":383},"_2-do-they-design-for-permissions-and-governance","2. Do they design for permissions and governance?",[100,386,387],{},"Check whether the vendor can implement least privilege, action allowlists, human approval for sensitive operations, credential management, and full auditing. Above all, an agent must not be allowed to inherit the excessive permissions of a shared account.",[374,389,391],{"id":390},"_3-do-they-provide-observability","3. Do they provide observability?",[100,393,394],{},"A production system needs to show why the agent made a decision, which tools it called, what data it used, how much it cost, and at which step it failed. Chat logs alone are not enough to diagnose operational problems.",[374,396,398],{"id":397},"_4-can-the-model-be-replaced","4. Can the model be replaced?",[100,400,401],{},"Model prices, policies, and capabilities change. A good architecture separates the model provider from the business process and keeps a fixed test set and rollback mechanism, rather than hard-coding key processes into one model's proprietary features.",[374,403,405],{"id":404},"_5-do-the-security-and-deployment-approach-meet-your-requirements","5. Do the security and deployment approach meet your requirements?",[100,407,408],{},"The vendor should ask first about data classification, retention periods, service regions, provider terms, private deployment needs, and incident reporting. A promise that \"data will not leak\" without technical and contractual measures behind it is not a complete solution.",[124,410,412],{"id":411},"the-three-most-common-failures","The three most common failures",[374,414,416],{"id":415},"no-boundaries-on-scope","No boundaries on scope",[100,418,419],{},"The agent is expected from day one to handle every customer service, purchasing, and operations issue, and in the end no scenario has a definition of done. The right approach is to start with one task that has clear inputs, outputs, and an owner.",[374,421,423],{"id":422},"no-human-review-for-high-risk-actions","No human review for high-risk actions",[100,425,426],{},"Just because an agent can generate payment, deletion, or notification instructions does not mean they should be executed directly. High-risk actions need human approval, amount or frequency limits, and a way to reverse them.",[374,428,430],{"id":429},"the-data-simply-is-not-usable","The data simply is not usable",[100,432,433],{},"When documents are outdated, field definitions are inconsistent, and permissions are a mess, an agent will only amplify the problems faster. Sample and validate data quality before the PoC, and assign responsibility for data maintenance once in production.",[124,435,437],{"id":436},"accept-on-tasks-not-just-answers","Accept on tasks, not just answers",[100,439,440],{},"AI agent acceptance should cover at least:",[442,443,444,447,450,453,456,459],"ul",{},[339,445,446],{},"Task completion rate and accuracy of key fields.",[339,448,449],{},"Whether tool selection, parameters, and execution order are correct.",[339,451,452],{},"Whether it can stop safely when permissions are insufficient, a service times out, or data is missing.",[339,454,455],{},"Which scenarios must be handed to a person, and whether the handover information is complete.",[339,457,458],{},"Whether the cost per task and at peak traffic stays within the ceiling.",[339,460,461],{},"Whether the fixed test set still passes after a model or prompt update.",[124,463,465],{"id":464},"conclusion-an-agent-is-an-engineered-system-not-a-model-demo","Conclusion: An agent is an engineered system, not a model demo",[100,467,468],{},"Model capability matters, but the success of an enterprise AI agent depends more on integration, governance, monitoring, and clear boundaries of responsibility. Platforms lower the barrier to experimentation, and custom development handles differentiated processes; the truly mature approach is to choose a combination based on risk rather than assuming one option is always best.",[100,470,471],{},"When choosing a vendor, ask them to use your real processes to explain how permissions are restricted, how failures are recovered, how costs are estimated, and how models will be swapped in the future. Only a team that can answer these questions is able to turn a demo into an enterprise system that runs for the long term.",[124,473,475],{"id":474},"frequently-asked-questions-about-ai-agent-development","Frequently Asked Questions About AI Agent Development",[374,477,479],{"id":478},"how-much-does-ai-agent-development-cost","How much does AI agent development cost?",[100,481,482],{},"Based on custom projects in Taiwan in 2026, a single-use-case PoC costs around NT$300K–800K, a production launch around NT$800K–2.5M, and multi-agent or cross-system workflows usually start at NT$2.5M. The actual figure still depends on the data, permissions, integrations, and acceptance requirements.",[374,484,486],{"id":485},"what-is-the-difference-between-an-ai-agent-and-a-chatbot","What is the difference between an AI agent and a chatbot?",[100,488,489],{},"A chatbot mainly understands questions and replies with content. An AI agent also plans steps toward a goal, calls tools, reads and writes to systems, and moves the task forward, so it needs stricter governance.",[374,491,493],{"id":492},"how-long-does-an-ai-agent-project-take","How long does an AI agent project take?",[100,495,496],{},"A small PoC typically takes about 4 to 8 weeks, a single production use case about 2 to 4 months, and multi-system or multi-agent projects usually longer.",[374,498,500],{"id":499},"how-should-a-company-choose-its-first-ai-agent-use-case","How should a company choose its first AI agent use case?",[100,502,503],{},"Prioritize processes that are high-frequency, have clear rules, use available data, and are easy for people to verify, and make sure there is a clear owner.",[374,505,507],{"id":506},"could-an-ai-agent-raise-its-own-permissions","Could an AI agent raise its own permissions?",[100,509,510],{},"A production system should not allow it. Restrict actions with least privilege, allowlists, short-lived credentials, and human approval.",[374,512,514],{"id":513},"what-other-monthly-costs-does-an-ai-agent-have","What other monthly costs does an AI agent have?",[100,516,517],{},"Beyond model fees, there are databases, third-party APIs, computing, monitoring, logging, security, and operations staff.",[374,519,521],{"id":520},"if-we-already-have-chatgpt-enterprise-do-we-still-need-a-custom-ai-agent","If we already have ChatGPT Enterprise, do we still need a custom AI agent?",[100,523,524],{},"For personal Q&A you may not. If the agent has to connect to internal systems and run processes, you usually still need custom integration and governance.",[374,526,528],{"id":527},"can-an-ai-agent-be-deployed-privately","Can an AI agent be deployed privately?",[100,530,531],{},"Yes, but you must also plan for inference hardware, data, monitoring, updates, security, and operational responsibility.",[374,533,535],{"id":534},"how-should-we-choose-between-an-ai-agent-platform-and-custom-development","How should we choose between an AI agent platform and custom development?",[100,537,538],{},"Standard processes and quick validation suit a platform; special permissions, complex integration, or differentiated capabilities are better served by custom or hybrid solutions.",[374,540,542],{"id":541},"how-should-an-ai-agent-be-accepted","How should an AI agent be accepted?",[100,544,545],{},"Validate task completion, tool calls, exception recovery, permission blocks, human takeover, and cost, not just whether the answers read smoothly.",[374,547,549],{"id":548},"will-changing-models-in-the-future-mean-starting-over","Will changing models in the future mean starting over?",[100,551,552],{},"If you build a model interface layer, a fixed test set, and rollback mechanisms in advance, the cost of switching models can be kept down.",[374,554,556],{"id":555},"what-kind-of-ai-agent-projects-is-joinx-a-good-fit-for","What kind of AI agent projects is JoinX a good fit for?",[100,558,559],{},"JoinX is a good fit for enterprise projects that need to integrate with existing systems, permissions, and real operational processes, and that require results to be verifiable, auditable, and maintainable.",{"title":561,"searchDepth":562,"depth":562,"links":563},"",2,[564,565,566,567,575,580,581,582],{"id":126,"depth":562,"text":127},{"id":236,"depth":562,"text":237},{"id":330,"depth":562,"text":331},{"id":368,"depth":562,"text":369,"children":568},[569,571,572,573,574],{"id":376,"depth":570,"text":377},3,{"id":383,"depth":570,"text":384},{"id":390,"depth":570,"text":391},{"id":397,"depth":570,"text":398},{"id":404,"depth":570,"text":405},{"id":411,"depth":562,"text":412,"children":576},[577,578,579],{"id":415,"depth":570,"text":416},{"id":422,"depth":570,"text":423},{"id":429,"depth":570,"text":430},{"id":436,"depth":562,"text":437},{"id":464,"depth":562,"text":465},{"id":474,"depth":562,"text":475,"children":583},[584,585,586,587,588,589,590,591,592,593,594,595],{"id":478,"depth":570,"text":479},{"id":485,"depth":570,"text":486},{"id":492,"depth":570,"text":493},{"id":499,"depth":570,"text":500},{"id":506,"depth":570,"text":507},{"id":513,"depth":570,"text":514},{"id":520,"depth":570,"text":521},{"id":527,"depth":570,"text":528},{"id":534,"depth":570,"text":535},{"id":541,"depth":570,"text":542},{"id":548,"depth":570,"text":549},{"id":555,"depth":570,"text":556},"Technology Insights","/images/blog/choose-ai-agent-company-2026.webp","2026/08/27","In 2026, AI agent development in Taiwan starts at around NT$300K, and enterprise multi-agent systems start at NT$2.5M. This article compares platform subscriptions with custom development and covers five vendor selection criteria, a cost overview, and frequently asked questions.","md",[602,603,605,607,609,611,613,615,617,619,621,623],{"question":479,"answer":482},{"question":486,"answer":604},"A chatbot mainly understands questions and replies with content. An AI agent also plans steps toward a goal, calls tools, reads and writes to systems, and moves the task forward. That is why agents need stricter permissions, auditing, exception handling, and human approval.",{"question":493,"answer":606},"A small PoC typically takes about 4 to 8 weeks, a single production use case about 2 to 4 months, and multi-system or multi-agent projects usually longer. Data availability, APIs, security reviews, and cross-department decisions all directly affect the timeline.",{"question":500,"answer":608},"Prioritize processes that are high-frequency, have relatively clear rules, use data that is available, and produce results that people can easily verify, such as internal knowledge lookup, ticket classification, or first-pass report preparation. Do not start with a cross-department core process that has no clear owner.",{"question":507,"answer":610},"It should not be allowed to. A production system should limit what can be executed through least privilege, short-lived credentials, action allowlists, and human approval, and log every tool call in full. A model proposing an action does not mean the system has to carry it out.",{"question":514,"answer":612},"Beyond model tokens, there are vector databases, search or OCR APIs, cloud computing, monitoring, logging, security tools, data updates, and operations staff. When assessing total cost of ownership, include peak usage and failed retries.",{"question":521,"answer":614},"If the need is personal Q&A and document collaboration, an enterprise tool may be enough. If the agent has to connect to ERP, CRM, or internal permissions and run processes automatically, you still need to design integration, governance, auditing, and acceptance, which usually falls within the scope of a custom system.",{"question":528,"answer":616},"Yes, but first confirm that the model, inference hardware, databases, monitoring, and update process can all run in the designated environment. Private deployment does not remove risk on its own; you still have to handle permissions, vulnerability patching, model versions, and operational responsibility.",{"question":535,"answer":618},"When the process is standard, integrations are limited, and you want to validate quickly, start with a platform. When special permissions, complex system integration, strict data boundaries, or long-term differentiation are involved, custom development is usually the better fit. A hybrid of a platform plus custom integration is also an option.",{"question":542,"answer":620},"Beyond answer quality, measure task completion rate, tool call accuracy, the share of cases that need human takeover, error recovery, permission blocks, and cost ceilings. Every key action should have a test case that can be replayed.",{"question":549,"answer":622},"If processes, tools, prompts, and the model are tightly coupled, switching will be expensive. A better design isolates the provider behind a model interface layer and keeps test sets, version history, and rollback mechanisms, so the model can be replaced once it passes acceptance.",{"question":556,"answer":624},"JoinX is a good fit for enterprise projects that need to connect AI agents to existing systems, permissions, and real operational processes. From use case review, PoC, and engineering integration through to launch and operations, the work is built to be verifiable, auditable, and easy to hand over.",true,{},"AI agent","/en/article/choose-ai-agent-company-2026",{"title":95,"description":599},null,"en/article/choose-ai-agent-company-2026","2026/07/23","blog","4MONYuY3FZGNg65wJp2AZzjJGZcZc5Eh3rT5nO9mjJk",[636,641,647],{"slug":637,"title":638,"time":639,"cover":640,"need":627},"2026-ai-agent-enterprise","What Is an AI Agent? Five Things to Confirm Before Your Enterprise Adopts One","2026/07/16","/images/blog/2026-ai-agent-enterprise.webp",{"slug":642,"title":643,"time":644,"cover":645,"need":646},"logistics-management-system-guide","How to Choose a Logistics Management System: Dispatch, Proof of Delivery, and Integration with Existing Systems","2026/10/08","/images/blog/logistics-management-system-guide.webp","logistics system",{"slug":648,"title":649,"time":650,"cover":651,"need":652},"order-management-system-guide","How to Choose an Order Management System: Off-the-Shelf Software, ERP, and Custom Development Compared","2026/10/07","/images/blog/order-management-system-guide.webp","order management system",1792411067191]