[{"data":1,"prerenderedAt":698},["ShallowReactive",2],{"article-ai-agent-vs-workflow-vs-orchestrator":3,"related-articles-ai-agent-vs-workflow-vs-orchestrator":166},{"id":4,"title":5,"author":6,"body":7,"category":140,"description":141,"extension":142,"featured":143,"heroImage":144,"meta":145,"navigation":143,"path":146,"publishedAt":147,"readingTime":148,"relatedAgents":149,"relatedArticles":152,"relatedWorkflows":155,"searchIntent":158,"seo":159,"stem":160,"topics":161,"updatedAt":147,"visual":21,"__hash__":165},"articles\u002Farticles\u002Fai-agent-vs-workflow-vs-orchestrator.md","AI agent vs. workflow vs. orchestrator: what is the difference?","StackOS team",{"type":8,"value":9,"toc":130},"minimark",[10,14,17,23,28,31,34,43,47,50,53,56,60,63,66,74,78,81,104,107,111,114,117,121,124,127],[11,12,13],"p",{},"An AI agent performs a focused role. A workflow defines the stages and rules of the work. An orchestrator coordinates the workflow, choosing the right agents, context, and tools as the job changes.",[11,15,16],{},"The terms are often used interchangeably, but separating them makes AI-powered work much easier to design and manage.",[18,19],"article-concept-visual",{"caption":20,"mode":21,"title":22},"Agents perform focused roles. The workflow defines the path. The orchestrator keeps the complete job moving.","roles","One job, three different responsibilities",[24,25,27],"h2",{"id":26},"what-is-an-ai-agent","What is an AI agent?",[11,29,30],{},"An AI agent is a model operating with a role, instructions, context, and tools. Its role should be focused enough that its decisions can be understood and checked.",[11,32,33],{},"A content workflow might use an evidence curator, a writer, and a claim reviewer. A delivery workflow might use a designer, implementer, tester, and reviewer. These specialists can use the same underlying model while having different responsibilities and boundaries.",[11,35,36,37,42],{},"See the ",[38,39,41],"a",{"href":40},"\u002Flibrary\u002Fagents","agent library"," for examples of focused roles used by StackOS workflows.",[24,44,46],{"id":45},"what-is-a-workflow","What is a workflow?",[11,48,49],{},"A workflow is the repeatable shape of the work. It defines the stages that matter, what each stage needs, which stages depend on others, where approval is required, and what a successful result looks like.",[11,51,52],{},"The workflow is reusable, but each job is specific. “Produce a campaign” may always require research, planning, creation, and review, while the actual steps expand based on the channels, source material, and goals in the request.",[11,54,55],{},"This is why a workflow is more useful than a long prompt. It holds the relationships and state around the work, not only instructions for one response.",[24,57,59],{"id":58},"what-is-an-orchestrator","What is an orchestrator?",[11,61,62],{},"An orchestrator is the coordinating role for the complete job. It understands the workflow, assembles the right context, brings in specialists at the right time, protects approval boundaries, and keeps the plan consistent as new information appears.",[11,64,65],{},"It does not need to perform every task itself. In fact, strong coordination usually separates creation from review so the same role is not judging its own work.",[11,67,68,69,73],{},"The ",[38,70,72],{"href":71},"\u002Flibrary\u002Forchestrators","orchestrator library"," shows how StackOS coordinates content, engineering, and marketing work.",[24,75,77],{"id":76},"how-do-they-work-together","How do they work together?",[11,79,80],{},"Think of producing a campaign:",[82,83,84,92,98],"ul",{},[85,86,68,87,91],"li",{},[88,89,90],"strong",{},"workflow"," says research must finish before the angle is selected, and review must finish before publishing.",[85,93,68,94,97],{},[88,95,96],{},"agents"," research the evidence, shape the plan, write the assets, and review the claims.",[85,99,68,100,103],{},[88,101,102],{},"orchestrator"," makes sure each specialist receives the right input, updates the plan when the campaign changes, and stops at the approval point before anything goes live.",[11,105,106],{},"StackOS keeps these parts connected to the actual apps where the work happens. The AI client remains the place where you ask and direct. StackOS becomes the place where the complete job stays organized.",[24,108,110],{"id":109},"do-you-need-multiple-models","Do you need multiple models?",[11,112,113],{},"No. Different agents are roles, not necessarily different models. One model can take several roles at different stages, or a team can choose different models for different strengths.",[11,115,116],{},"The more important separation is responsibility. A focused research role should preserve sources. A review role should be able to challenge the draft. An orchestrator should coordinate without quietly bypassing approvals.",[24,118,120],{"id":119},"which-part-should-you-design-first","Which part should you design first?",[11,122,123],{},"Start with the outcome and the workflow. Ask what must be true before the work is considered done, where mistakes are expensive, and which stages depend on earlier evidence.",[11,125,126],{},"Then assign focused agents to those responsibilities. Add an orchestrator when the work spans several stages, specialists, tools, or sessions.",[11,128,129],{},"This order keeps the system grounded in real work. You are not collecting agents because they sound impressive; you are giving each necessary part of the job a clear owner.",{"title":131,"searchDepth":132,"depth":132,"links":133},"",2,[134,135,136,137,138,139],{"id":26,"depth":132,"text":27},{"id":45,"depth":132,"text":46},{"id":58,"depth":132,"text":59},{"id":76,"depth":132,"text":77},{"id":109,"depth":132,"text":110},{"id":119,"depth":132,"text":120},"AI operations","Agents perform focused roles, workflows define how work moves, and orchestrators coordinate the whole job. Here is how the three fit together.","md",true,null,{},"\u002Farticles\u002Fai-agent-vs-workflow-vs-orchestrator","2026-07-09","6 min read",[150,151],"branding-narrative-writer","branding-claim-auditor",[153,154],"what-is-an-agentic-workflow","use-codex-claude-gemini-with-existing-tools",[156,157],"branding-content-production","engineering-tracked-delivery","Compare AI agents, workflows, and orchestrators in plain language",{"title":5,"description":141},"articles\u002Fai-agent-vs-workflow-vs-orchestrator",[162,163,164],"AI agents","orchestrators","agentic workflows","5k8G3D6QZtrxhpl399L2aCDCRmqFC_hRSslJnk7_cVo",[167,252,383,518],{"id":4,"title":5,"author":6,"body":168,"category":140,"description":141,"extension":142,"featured":143,"heroImage":144,"meta":246,"navigation":143,"path":146,"publishedAt":147,"readingTime":148,"relatedAgents":247,"relatedArticles":248,"relatedWorkflows":249,"searchIntent":158,"seo":250,"stem":160,"topics":251,"updatedAt":147,"visual":21,"__hash__":165},{"type":8,"value":169,"toc":238},[170,172,174,176,178,180,182,186,188,190,192,194,196,198,200,204,206,208,222,224,226,228,230,232,234,236],[11,171,13],{},[11,173,16],{},[18,175],{"caption":20,"mode":21,"title":22},[24,177,27],{"id":26},[11,179,30],{},[11,181,33],{},[11,183,36,184,42],{},[38,185,41],{"href":40},[24,187,46],{"id":45},[11,189,49],{},[11,191,52],{},[11,193,55],{},[24,195,59],{"id":58},[11,197,62],{},[11,199,65],{},[11,201,68,202,73],{},[38,203,72],{"href":71},[24,205,77],{"id":76},[11,207,80],{},[82,209,210,214,218],{},[85,211,68,212,91],{},[88,213,90],{},[85,215,68,216,97],{},[88,217,96],{},[85,219,68,220,103],{},[88,221,102],{},[11,223,106],{},[24,225,110],{"id":109},[11,227,113],{},[11,229,116],{},[24,231,120],{"id":119},[11,233,123],{},[11,235,126],{},[11,237,129],{},{"title":131,"searchDepth":132,"depth":132,"links":239},[240,241,242,243,244,245],{"id":26,"depth":132,"text":27},{"id":45,"depth":132,"text":46},{"id":58,"depth":132,"text":59},{"id":76,"depth":132,"text":77},{"id":109,"depth":132,"text":110},{"id":119,"depth":132,"text":120},{},[150,151],[153,154],[156,157],{"title":5,"description":141},[162,163,164],{"id":253,"title":254,"author":6,"body":255,"category":365,"description":366,"extension":142,"featured":367,"heroImage":144,"meta":368,"navigation":143,"path":369,"publishedAt":147,"readingTime":370,"relatedAgents":371,"relatedArticles":373,"relatedWorkflows":374,"searchIntent":375,"seo":376,"stem":377,"topics":378,"updatedAt":147,"visual":266,"__hash__":382},"articles\u002Farticles\u002Fhow-ai-agents-use-accounts-safely.md","How can AI agents use business accounts without seeing the login?",{"type":8,"value":256,"toc":358},[257,260,263,268,272,275,278,282,285,288,292,295,298,312,315,319,322,325,329,332,347,350],[11,258,259],{},"AI agents can use business accounts without receiving the password or API key. The secret stays inside a trusted local process, while the model receives only a safe account reference and permission to request a specific action.",[11,261,262],{},"This boundary matters because an AI needs the ability to work, not a copy of every login.",[18,264],{"caption":265,"mode":266,"title":267},"Private account details stay inside the local StackOS process while the AI receives only the safe context and result it needs.","security","The model directs. StackOS performs the approved action.",[24,269,271],{"id":270},"what-should-the-model-receive","What should the model receive?",[11,273,274],{},"The model needs enough information to make a good decision: which account is available, what it can do, whether it is ready, and what approval is required.",[11,276,277],{},"It does not need the raw token, password, or private key. StackOS provides a safe reference that identifies the connection without exposing the secret.",[24,279,281],{"id":280},"where-does-the-secret-stay","Where does the secret stay?",[11,283,284],{},"StackOS runs locally on the user’s Mac. Connected credentials are resolved inside that local process only when an explicit action is being performed.",[11,286,287],{},"The model asks for an intent-level action—such as publishing an approved post to a selected site. StackOS checks the workflow permission, selected account, action contract, and approval before making the call.",[24,289,291],{"id":290},"what-prevents-an-agent-from-doing-anything-it-wants","What prevents an agent from doing anything it wants?",[11,293,294],{},"Access is scoped to the work. A workflow stage receives only the tools allowed for that stage, and sensitive or costly actions can require approval.",[11,296,297],{},"That creates several useful boundaries:",[82,299,300,303,306,309],{},[85,301,302],{},"A research stage can read approved information without receiving publishing access.",[85,304,305],{},"A writer can prepare content without being allowed to send it.",[85,307,308],{},"A review stage can inspect the result without changing external systems.",[85,310,311],{},"A publishing stage can use one approved account for one approved action.",[11,313,314],{},"Each action is recorded with its result so a person can understand what happened later.",[24,316,318],{"id":317},"is-local-software-enough-by-itself","Is local software enough by itself?",[11,320,321],{},"Running locally reduces how far secrets have to travel, but location is only part of the design. Safe agent access also needs narrow permissions, explicit actions, approval gates, useful error handling, and a complete history.",[11,323,324],{},"The goal is not to claim that an AI agent can never make a mistake. The goal is to make its authority understandable, bounded, and reviewable.",[24,326,328],{"id":327},"what-should-teams-ask-before-connecting-an-account","What should teams ask before connecting an account?",[11,330,331],{},"Ask four questions:",[333,334,335,338,341,344],"ol",{},[85,336,337],{},"Which exact actions does this connection allow?",[85,339,340],{},"Which workflow stages can request those actions?",[85,342,343],{},"Which actions require a person to approve them?",[85,345,346],{},"What evidence will be recorded after the action runs?",[11,348,349],{},"If those answers are unclear, the connection is too broad.",[11,351,352,353,357],{},"StackOS is built around this local trust boundary. The ",[38,354,356],{"href":355},"\u002Flibrary\u002Fworkflows","workflow library"," shows where connected actions fit inside complete, visible work rather than appearing as isolated tool calls.",{"title":131,"searchDepth":132,"depth":132,"links":359},[360,361,362,363,364],{"id":270,"depth":132,"text":271},{"id":280,"depth":132,"text":281},{"id":290,"depth":132,"text":291},{"id":317,"depth":132,"text":318},{"id":327,"depth":132,"text":328},"Security","A local action layer can keep private credentials away from the model while still letting AI perform explicit, approved work in connected apps.",false,{},"\u002Farticles\u002Fhow-ai-agents-use-accounts-safely","5 min read",[372],"branding-sanitization-reviewer",[154,153],[157,156],"Understand how AI agents can use connected accounts without receiving credentials",{"title":254,"description":366},"articles\u002Fhow-ai-agents-use-accounts-safely",[379,380,381],"AI agent security","credentials","local software","ct8Jul2KFiiCNRPoR13www3awDGh2JJjamtceucT8_I",{"id":384,"title":385,"author":6,"body":386,"category":500,"description":501,"extension":142,"featured":143,"heroImage":144,"meta":502,"navigation":143,"path":503,"publishedAt":147,"readingTime":148,"relatedAgents":504,"relatedArticles":505,"relatedWorkflows":507,"searchIntent":509,"seo":510,"stem":511,"topics":512,"updatedAt":147,"visual":397,"__hash__":517},"articles\u002Farticles\u002Fuse-codex-claude-gemini-with-existing-tools.md","How to use Codex, Claude Code, or Gemini with the tools you already have",{"type":8,"value":387,"toc":492},[388,391,394,399,403,406,409,413,416,419,422,426,429,432,436,439,442,446,449,471,474,478,481],[11,389,390],{},"You do not need to replace Codex, Claude Code, Gemini, or the business apps your team already uses. Connect the AI client to StackOS, connect StackOS to your approved tools, and keep working from the interface you prefer.",[11,392,393],{},"This creates a clean division of responsibility: the AI understands and directs the work; StackOS organizes its plan, state, tool access, approvals, and history.",[18,395],{"caption":396,"mode":397,"title":398},"StackOS connects the conversation to a visible plan and the approved tools that complete it.","connections","Keep the AI client. Keep the apps.",[24,400,402],{"id":401},"why-keep-the-ai-client-separate","Why keep the AI client separate?",[11,404,405],{},"AI clients improve quickly and people have different preferences. One person may work in Codex, another in Claude Code, and another through Gemini. Locking the whole operating process to one chat interface makes switching expensive and fragments the work.",[11,407,408],{},"A shared work layer lets the client change without losing the project’s workflows, connected apps, or history.",[24,410,412],{"id":411},"what-does-the-connection-look-like","What does the connection look like?",[11,414,415],{},"The AI client connects to StackOS through a standard tool interface. When you make a request, the model can find the relevant workflow, adapt it to the request, and present the plan.",[11,417,418],{},"After approval, work moves step by step. When a stage needs an outside app—such as Slack, Shopify, WordPress, an ad platform, or an analytics tool—StackOS performs the specific approved action and returns a safe result to the AI.",[11,420,421],{},"Your private login stays in the local StackOS process. The model receives a safe reference and the result it needs, not the secret itself.",[24,423,425],{"id":424},"does-stackos-replace-automation-tools","Does StackOS replace automation tools?",[11,427,428],{},"StackOS is designed to coordinate work across existing tools. It does not ask a content team to abandon WordPress, a commerce team to replace Shopify, or an engineering team to stop using GitHub.",[11,430,431],{},"It adds the missing continuity between the request and those tools: which workflow applies, what is currently ready, what needs review, what action was taken, and where the result belongs.",[24,433,435],{"id":434},"what-happens-when-you-change-models","What happens when you change models?",[11,437,438],{},"The durable work remains in StackOS. A new compatible AI client can recover the project, current plan, completed steps, decisions, and next actions instead of relying on the memory of one chat thread.",[11,440,441],{},"That makes model choice a practical preference rather than an operating-system decision.",[24,443,445],{"id":444},"a-simple-example","A simple example",[11,447,448],{},"Suppose you ask Codex to investigate customer feedback and prepare a fix.",[333,450,451,459,462,465,468],{},[85,452,453,454,458],{},"Codex identifies the request and opens the appropriate ",[38,455,457],{"href":456},"\u002Flibrary\u002Fworkflows\u002Fcommunications-customer-feedback-intake","customer feedback workflow",".",[85,460,461],{},"StackOS gathers the approved conversation context and creates visible work.",[85,463,464],{},"The investigation uses the connected communication and project tools.",[85,466,467],{},"If delivery is needed, the result moves into a tracked delivery workflow with its dependencies intact.",[85,469,470],{},"You can review what happened, what remains, and the evidence behind the conclusion.",[11,472,473],{},"The same pattern works when the starting client is Claude Code or Gemini.",[24,475,477],{"id":476},"what-do-you-need-to-get-started","What do you need to get started?",[11,479,480],{},"Install StackOS on your Mac, connect a supported AI client, and add the apps you want it to use. Start with one real workflow that matters to your team rather than trying to automate everything at once.",[11,482,483,484,486,487,491],{},"Browse the ",[38,485,356],{"href":355}," to choose a starting point, or ",[38,488,490],{"href":489},"\u002F#install","download StackOS for Mac"," and connect the client you already use.",{"title":131,"searchDepth":132,"depth":132,"links":493},[494,495,496,497,498,499],{"id":401,"depth":132,"text":402},{"id":411,"depth":132,"text":412},{"id":424,"depth":132,"text":425},{"id":434,"depth":132,"text":435},{"id":444,"depth":132,"text":445},{"id":476,"depth":132,"text":477},"Getting started","Keep your preferred AI client and existing business apps. Add a shared work layer so requests become trackable plans with safe, explicit actions.",{},"\u002Farticles\u002Fuse-codex-claude-gemini-with-existing-tools",[],[153,506],"how-ai-agents-use-accounts-safely",[508,157],"communications-customer-feedback-intake","Learn how to connect an existing AI client to existing business tools",{"title":385,"description":501},"articles\u002Fuse-codex-claude-gemini-with-existing-tools",[513,514,515,516],"Codex","Claude Code","Gemini","AI tools","C43pDZZGuDV2XSUh3op_ayhHUJJ2UYL1TSE6kjNv3lI",{"id":519,"title":520,"author":6,"body":521,"category":681,"description":682,"extension":142,"featured":143,"heroImage":144,"meta":683,"navigation":143,"path":684,"publishedAt":147,"readingTime":685,"relatedAgents":686,"relatedArticles":688,"relatedWorkflows":690,"searchIntent":692,"seo":693,"stem":694,"topics":695,"updatedAt":147,"visual":90,"__hash__":697},"articles\u002Farticles\u002Fwhat-is-an-agentic-workflow.md","What is an agentic workflow? A practical guide to AI-powered work",{"type":8,"value":522,"toc":673},[523,526,537,541,545,548,551,554,586,590,593,601,604,607,611,614,617,620,624,627,647,653,657,660,663,667,670],[11,524,525],{},"An agentic workflow is a structured way for an AI agent to move from a goal to a completed, checked result. It gives the AI a sequence of steps, the right context and tools for each step, clear approval points, and a record of what happened.",[11,527,528,529,533,534,536],{},"The important word is not ",[530,531,532],"em",{},"AI",". It is ",[530,535,90],{},". A capable model can reason about a request, but real work also needs continuity: what is ready, what is waiting, who approved a decision, which app was changed, and what evidence proves the result.",[538,539],"article-workflow-visual",{"title":540,"workflow":156},"A complete content workflow, from request to published result",[24,542,544],{"id":543},"what-makes-a-workflow-agentic","What makes a workflow agentic?",[11,546,547],{},"An ordinary automation follows a fixed rule: when this happens, do that. An agentic workflow can interpret the goal, adapt the plan to the situation, use the right specialist for each stage, and respond when new information changes the work.",[11,549,550],{},"The workflow still provides boundaries. The AI does not get a blank check. It works through named stages, uses approved connections, and pauses when a person needs to decide.",[11,552,553],{},"A useful agentic workflow usually includes five parts:",[333,555,556,562,568,574,580],{},[85,557,558,561],{},[88,559,560],{},"A goal."," The outcome the person actually wants, written in business language.",[85,563,564,567],{},[88,565,566],{},"A plan."," The steps, relationships, checks, and approval points needed for this particular request.",[85,569,570,573],{},[88,571,572],{},"Specialists."," Focused AI roles for research, creation, review, or other distinct responsibilities.",[85,575,576,579],{},[88,577,578],{},"Connected tools."," The apps and accounts where the work already happens.",[85,581,582,585],{},[88,583,584],{},"A durable work record."," Status, decisions, outputs, dependencies, and evidence that remain available after the chat ends.",[24,587,589],{"id":588},"what-happens-from-request-to-result","What happens from request to result?",[11,591,592],{},"Imagine a content leader asks Claude Code to turn customer research into a four-week campaign. The conversation is only the starting point.",[11,594,595,596,600],{},"StackOS recognizes the matching ",[38,597,599],{"href":598},"\u002Flibrary\u002Fworkflows\u002Fbranding-content-production","content production workflow",", adds the right stages for the request, and presents the complete plan before work begins. A research specialist gathers the source material. A strategist shapes the angle and channel plan. A writer creates the main piece. Separate reviewers check claims, voice, and disclosure risk. Approved work then moves to the connected publishing tools.",[11,602,603],{},"Each stage changes state as it moves from waiting to working, review, and done. If the research is incomplete, later work stays blocked. If a reviewer finds an unsupported claim, the piece returns to the right stage with the reason attached.",[11,605,606],{},"That visible state is what turns a promising AI conversation into dependable work.",[24,608,610],{"id":609},"how-is-this-different-from-a-chatbot","How is this different from a chatbot?",[11,612,613],{},"A chatbot mainly responds inside a conversation. It may produce an excellent answer, but the person still has to carry the result into other apps, remember what remains, and explain the context again next time.",[11,615,616],{},"An agentic workflow gives the conversation somewhere to go. The request becomes organized work with owners, dependencies, connected actions, approvals, and proof.",[11,618,619],{},"You can still use the AI interface you prefer. StackOS works with Codex, Claude Code, Gemini, and other tool-using AI clients; it adds the shared work layer around them.",[24,621,623],{"id":622},"where-can-agentic-workflows-be-used","Where can agentic workflows be used?",[11,625,626],{},"The pattern is not limited to software development. Any repeatable outcome with judgment, multiple steps, connected apps, or approval risk can benefit.",[82,628,629,632,635,638,641,644],{},[85,630,631],{},"Engineering teams can move from a feature request through design, delivery, testing, and review.",[85,633,634],{},"Content teams can research, draft, fact-check, create visuals, and publish.",[85,636,637],{},"Finance teams can collect data, investigate a variance, prepare a recommendation, and route it for approval.",[85,639,640],{},"Commerce teams can update product content, review merchandising changes, and coordinate Shopify operations.",[85,642,643],{},"Sales teams can research accounts, enrich leads, prepare outreach, and keep the CRM accurate.",[85,645,646],{},"Support teams can investigate an issue and hand confirmed work to the right delivery team.",[11,648,649,650,652],{},"Explore the ",[38,651,356],{"href":355}," to see how the same structure adapts across different kinds of work.",[24,654,656],{"id":655},"when-should-you-use-one","When should you use one?",[11,658,659],{},"Use an agentic workflow when the work needs more than one answer. Strong signals include handoffs between people or apps, steps that depend on earlier results, sensitive actions, required review, and work that may continue across multiple sessions.",[11,661,662],{},"For a quick question or one-off draft, a normal conversation may be enough. For work that must finish correctly and remain understandable later, a workflow gives the AI—and the team—the structure it needs.",[24,664,666],{"id":665},"the-shortest-useful-definition","The shortest useful definition",[11,668,669],{},"An agentic workflow is a goal-driven plan that lets AI complete multi-step work through connected tools while keeping progress, approvals, and results visible.",[11,671,672],{},"That is the practical promise: keep the AI tool you already like, and give its work a reliable path from request to result.",{"title":131,"searchDepth":132,"depth":132,"links":674},[675,676,677,678,679,680],{"id":543,"depth":132,"text":544},{"id":588,"depth":132,"text":589},{"id":609,"depth":132,"text":610},{"id":622,"depth":132,"text":623},{"id":655,"depth":132,"text":656},{"id":665,"depth":132,"text":666},"Agentic workflows","An agentic workflow turns a goal into visible steps that AI can plan, complete, check, and hand off across the tools your team already uses.",{},"\u002Farticles\u002Fwhat-is-an-agentic-workflow","7 min read",[687,150],"branding-evidence-curator",[689,154],"ai-agent-vs-workflow-vs-orchestrator",[157,156,691],"marketing-campaign-production","Understand what an agentic workflow is, how it works, and when to use one",{"title":520,"description":682},"articles\u002Fwhat-is-an-agentic-workflow",[164,162,696],"workflow automation","p3F0WaG5bUQyMS33NZdcvFDy40J9TTEmz_HtpsjB0Lw",1783722297280]