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Droplet","/runners/do-droplet","23.runners/12.do-droplet",{"title":714,"path":715,"stem":716,"icon":285},"Magalu Cloud VM","/runners/magalu-vm","23.runners/13.magalu-vm",{"title":101,"path":718,"stem":719,"icon":720},"/runners/devkit","23.runners/14.devkit","i-lucide-laptop",{"title":722,"path":723,"stem":724,"icon":725},"Purposes & integrations","/runners/purposes","23.runners/16.purposes","i-lucide-layers",{"title":727,"path":728,"stem":729,"icon":241},"Fargate","/runners/fargate","23.runners/2.fargate",{"title":731,"path":732,"stem":733,"icon":241},"EC2 Auto Scaling","/runners/ec2-auto-scaling","23.runners/3.ec2-auto-scaling",{"title":735,"path":736,"stem":737,"icon":241},"EC2 VM","/runners/ec2-vm","23.runners/4.ec2-vm",{"title":287,"path":739,"stem":740,"icon":290},"/runners/kubernetes","23.runners/5.kubernetes",{"title":742,"path":743,"stem":744,"icon":280},"Azure Container Apps","/runners/azure-container-apps","23.runners/6.azure-container-apps",{"title":746,"path":747,"stem":748,"icon":280},"Azure VMSS","/runners/azure-vmss","23.runners/7.azure-vmss",{"title":750,"path":751,"stem":752,"icon":280},"Azure VM","/runners/azure-vm","23.runners/8.azure-vm",{"title":754,"path":755,"stem":756,"icon":275},"GCE","/runners/gce","23.runners/9.gce",{"title":758,"path":759,"stem":760,"children":761,"icon":763},"Chat","/chat","24.chat/01.index",[762,764,768,773,777,782,787,791,796,800,805,810,814,819,824,828,832,836,840,845],{"title":758,"path":759,"stem":760,"icon":763},"i-lucide-messages-square",{"title":765,"path":766,"stem":767,"icon":446},"Enable and publish","/chat/enable-and-publish","24.chat/02.enable-and-publish",{"title":769,"path":770,"stem":771,"icon":772},"Theme and customization","/chat/theme-and-customization","24.chat/03.theme-and-customization","i-lucide-palette",{"title":774,"path":775,"stem":776,"icon":85},"Members and moderation","/chat/members-and-moderation","24.chat/04.members-and-moderation",{"title":778,"path":779,"stem":780,"icon":781},"Channels","/chat/channels","24.chat/05.channels","i-lucide-hash",{"title":783,"path":784,"stem":785,"icon":786},"Using the chat","/chat/using-the-chat","24.chat/06.using-the-chat","i-lucide-message-circle",{"title":788,"path":789,"stem":790,"icon":44},"Actors in chat","/chat/actors-in-chat","24.chat/07.actors-in-chat",{"title":792,"path":793,"stem":794,"icon":795},"Internal support actor","/chat/internal-support-actor","24.chat/08.internal-support-actor","i-lucide-life-buoy",{"title":797,"path":798,"stem":799,"icon":104},"API and console","/chat/api-and-console","24.chat/09.api-and-console",{"title":801,"path":802,"stem":803,"icon":804},"Limits","/chat/limits","24.chat/10.limits","i-lucide-triangle-alert",{"title":806,"path":807,"stem":808,"icon":809},"Structured chat with DGI","/chat/structured-chat","24.chat/11.structured-chat","i-lucide-layout-list",{"title":811,"path":812,"stem":813,"icon":109},"Option A: hosted chat with DGI","/chat/option-a-hosted-chat","24.chat/12.option-a-hosted-chat",{"title":815,"path":816,"stem":817,"icon":818},"Option B: embed the chat component","/chat/option-b-embed-component","24.chat/13.option-b-embed-component","i-lucide-component",{"title":820,"path":821,"stem":822,"icon":823},"Option C: custom client (wire contract)","/chat/option-c-custom-client","24.chat/14.option-c-custom-client","i-lucide-braces",{"title":825,"path":826,"stem":827,"icon":394},"Responder configuration reference","/chat/responder-reference","24.chat/15.responder-reference",{"title":829,"path":830,"stem":831,"icon":547},"Cards, live updates and proactive posts","/chat/cards-live-and-proactive","24.chat/16.cards-live-and-proactive",{"title":833,"path":834,"stem":835,"icon":16},"Structured chat security model","/chat/structured-chat-security","24.chat/17.structured-chat-security",{"title":837,"path":838,"stem":839,"icon":181},"Option D: any app or API (dgi.chat)","/chat/option-d-chat-api","24.chat/18.option-d-chat-api",{"title":841,"path":842,"stem":843,"icon":844},"Card catalog","/chat/card-catalog","24.chat/19.card-catalog","i-lucide-layout-grid",{"title":846,"path":847,"stem":848,"icon":849},"Living Surfaces","/chat/living-surfaces","24.chat/20.living-surfaces","i-lucide-sprout",{"title":851,"path":852,"stem":853,"children":854,"icon":61},"DGI","/dgi","25.dgi/1.index",[855,857,862,866,870,874],{"title":856,"path":852,"stem":853,"icon":61},"What is DGI",{"title":858,"path":859,"stem":860,"icon":861},"How DGI works","/dgi/how-it-works","25.dgi/2.how-it-works","i-lucide-cog",{"title":863,"path":864,"stem":865,"icon":844},"Interfaces","/dgi/interfaces","25.dgi/3.interfaces",{"title":867,"path":868,"stem":869,"icon":29},"Quickstart","/dgi/quickstart","25.dgi/4.quickstart",{"title":871,"path":872,"stem":873,"icon":16},"Trust and safety","/dgi/trust-and-safety","25.dgi/5.trust-and-safety",{"title":875,"path":876,"stem":877,"icon":878},"FAQ","/dgi/faq","25.dgi/6.faq","i-lucide-circle-help",{"id":880,"title":58,"body":881,"description":2022,"extension":2023,"links":2024,"meta":2025,"navigation":2026,"path":59,"seo":2027,"stem":60,"__hash__":2028},"docs/03.concepts/03.dgi.md",{"type":882,"value":883,"toc":2006},"minimark",[884,889,929,941,961,965,980,985,988,1006,1021,1025,1054,1059,1077,1090,1094,1097,1163,1166,1172,1176,1179,1605,1614,1617,1621,1643,1647,1654,1722,1732,1745,1749,1756,1761,1768,1772,1887,1891,1900,1904,1971,1975,2002],[885,886,888],"h2",{"id":887},"tldr","TL;DR",[890,891,892,900,906,912,918],"ul",{},[893,894,895,899],"li",{},[896,897,898],"strong",{},"DGI is the reasoning front door."," You give it a goal in plain language. It interprets the intent, selects capabilities by reading their schemas, assembles a DAG, and hands it to the engine.",[893,901,902,905],{},[896,903,904],{},"AI designs, the engine executes."," DGI never runs anything itself, and never invents workflow names. It selects only from the declared catalog.",[893,907,908,911],{},[896,909,910],{},"DGI and MCP share one catalog."," An assistant over MCP picks one type and starts it. DGI assembles many types into a plan. Same registry, same schemas, same engine.",[893,913,914,917],{},[896,915,916],{},"Good plans freeze into compositions."," A plan that works becomes a virtual workflow that runs with no model call.",[893,919,920,923,924,928],{},[896,921,922],{},"Status: alpha."," Structured answers (chat, the ",[925,926,927],"code",{},"dgi.chat"," API, Living Surfaces), reasoning, plan compilation and promote-to-composition ship today. Automatic end-to-end dispatch of multi-step plans is on the roadmap.",[930,931,932],"tip",{},[933,934,935,936,940],"p",{},"New to DGI? Start with the ",[937,938,939],"a",{"href":852},"DGI section",": what it is, how a request becomes a card, every interface, and a quickstart. This page covers how DGI plans multi-step work.",[933,942,943,944,947,948,952,953,956,957,960],{},"DGI stands for ",[896,945,946],{},"Dialog Generative Interface",": the natural-language, dialog-driven surface in front of the engine. Older material sometimes reads it as ",[949,950,951],"em",{},"Dynamic Goal Interpreter",". Same component; that reading describes what it does: it interprets a ",[949,954,955],{},"goal"," through a ",[949,958,959],{},"dialog",".",[885,962,964],{"id":963},"where-dgi-sits","Where DGI sits",[933,966,967,968,971,972,975,976,979],{},"Everything on Orkestia resolves to one primitive: a ",[896,969,970],{},"workflow",". DGI does not invent a parallel execution path. It produces the same workflows that the ",[937,973,974],{"href":42},"MCP server"," exposes to assistants, that ",[937,977,978],{"href":64},"Staff"," actors run, and that you can compose by hand.",[981,982],"dag-diagram",{":edges":983,":nodes":984},"[{\"from\":\"goal\",\"to\":\"dgi\"},{\"from\":\"dgi\",\"to\":\"intent\",\"label\":\"interpret\"},{\"from\":\"intent\",\"to\":\"candidates\",\"label\":\"select via schemas\"},{\"from\":\"candidates\",\"to\":\"dag\",\"label\":\"assemble\"},{\"from\":\"dag\",\"to\":\"engine\",\"label\":\"execute\"},{\"from\":\"dag\",\"to\":\"vw\",\"label\":\"if reusable: compile\",\"dashed\":true},{\"from\":\"vw\",\"to\":\"engine\",\"label\":\"replay, no re-reasoning\",\"dashed\":true},{\"from\":\"engine\",\"to\":\"lumen\"}]","[{\"id\":\"goal\",\"label\":\"Natural-language goal / dialog\",\"kind\":\"start\"},{\"id\":\"dgi\",\"label\":\"DGI\",\"kind\":\"ai\"},{\"id\":\"intent\",\"label\":\"Intent + constraints\",\"kind\":\"data\"},{\"id\":\"candidates\",\"label\":\"Candidate workflows\",\"kind\":\"data\"},{\"id\":\"dag\",\"label\":\"Workflow DAG (plan)\",\"kind\":\"data\"},{\"id\":\"engine\",\"label\":\"Deterministic workflow engine\",\"kind\":\"engine\"},{\"id\":\"vw\",\"label\":\"Composition (virtual workflow)\",\"kind\":\"engine\"},{\"id\":\"lumen\",\"label\":\"Lumen\",\"kind\":\"data\"}]",[933,986,987],{},"Two internal components split the work:",[890,989,990,1000],{},[893,991,992,995,996,999],{},[896,993,994],{},"The reasoning core"," runs the configured LLM (through your org's AI provider connection), turns a goal into reasoning steps, and proposes candidate ",[896,997,998],{},"skills"," that map onto workflow types.",[893,1001,1002,1005],{},[896,1003,1004],{},"The plan compiler"," (the virtual engine) discovers installed workflows, assembles a layered DAG, validates it (structure, references, compatibility), and compiles it to engine config.",[933,1007,1008,1009,1012,1013,1016,1017,1020],{},"DGI draws its catalog from the same registry that backs the ",[937,1010,1011],{"href":165},"workflow types registry"," and MCP discovery. Its own operations are registered as ",[925,1014,1015],{},"dgi.*"," workflow types, so the reasoning loop runs ",[949,1018,1019],{},"through"," the engine, not beside it.",[885,1022,1024],{"id":1023},"the-dgi-loop","The DGI loop",[1026,1027,1028,1036,1042,1048],"card-group",{},[1029,1030,1033],"card",{"icon":1031,"title":1032},"i-lucide-message-square-text","1 · Interpret the goal",[933,1034,1035],{},"Extract intent, entities, and constraints from the message and prior turns. Ask when the goal is underspecified.",[1029,1037,1039],{"icon":581,"title":1038},"2 · Select capabilities",[933,1040,1041],{},"Match the intent against the org's catalog. Read each candidate's schema to know what it needs and produces.",[1029,1043,1045],{"icon":56,"title":1044},"3 · Assemble the plan",[933,1046,1047],{},"Wire selected workflows into a DAG ordered by data dependency. Validate the whole shape against schemas.",[1029,1049,1051],{"icon":624,"title":1050},"4 · Execute as a workflow",[933,1052,1053],{},"Hand the DAG to the engine. From here it is a normal run: event-sourced, observable, retryable.",[1055,1056,1058],"h3",{"id":1057},"_1-interpret-the-goal","1. Interpret the goal",[933,1060,1061,1062,1065,1066,1069,1070,1073,1074,960],{},"DGI starts from language, not a form. The message plus dialog state is parsed into a structured intent: ",[949,1063,1064],{},"what"," the user wants, the ",[949,1067,1068],{},"entities"," involved (a repo, a domain, an environment), and the ",[949,1071,1072],{},"constraints",". If a domain is named but no DNS provider is connected, or \"the repo\" is ambiguous, DGI surfaces the gap rather than guessing. That is the same discipline the MCP server enforces with ",[925,1075,1076],{},"rule://prerequisites-first",[930,1078,1079],{},[933,1080,1081,1082,1085,1086,1089],{},"A goal that names its nouns (\"deploy ",[925,1083,1084],{},"acme/marketing-site"," to ",[925,1087,1088],{},"acme.com","\") compiles to a tighter plan with fewer clarifying turns than a vague one (\"put my site online\").",[1055,1091,1093],{"id":1092},"_2-select-capabilities-through-schemas","2. Select capabilities through schemas",[933,1095,1096],{},"This stage keeps DGI grounded. Every workflow type publishes a typed input schema and declares its prerequisites. DGI selects capabilities by reading those schemas, the same way an assistant does over MCP:",[1098,1099,1100,1116],"table",{},[1101,1102,1103],"thead",{},[1104,1105,1106,1110,1113],"tr",{},[1107,1108,1109],"th",{},"Step",[1107,1111,1112],{},"Engine capability",[1107,1114,1115],{},"Purpose",[1117,1118,1119,1137,1150],"tbody",{},[1104,1120,1121,1125,1134],{},[1122,1123,1124],"td",{},"List",[1122,1126,1127,1130,1131],{},[925,1128,1129],{},"list_workflow_namespaces",", ",[925,1132,1133],{},"list_workflow_types",[1122,1135,1136],{},"Enumerate what the org is entitled to",[1104,1138,1139,1142,1147],{},[1122,1140,1141],{},"Inspect",[1122,1143,1144],{},[925,1145,1146],{},"get_workflow_schema",[1122,1148,1149],{},"Required inputs and outputs of a candidate",[1104,1151,1152,1155,1160],{},[1122,1153,1154],{},"Gate",[1122,1156,1157],{},[925,1158,1159],{},"get_workflow_prerequisites",[1122,1161,1162],{},"Resolve a missing connection or setup first",[933,1164,1165],{},"A step can only be wired into the DAG if its inputs can be satisfied: by the goal, by a connection, or by an earlier step's output.",[1167,1168,1169],"note",{},[933,1170,1171],{},"DGI never invents workflow names. If a capability is not registered for your org, it cannot appear in a plan.",[1055,1173,1175],{"id":1174},"_3-assemble-the-plan","3. Assemble the plan",[933,1177,1178],{},"With candidates and schemas known, DGI assembles a DAG. Each node is a workflow; each edge is a data dependency.",[1180,1181,1186],"pre",{"className":1182,"code":1183,"language":1184,"meta":1185,"style":1185},"language-json shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","{\n  \"goal\": \"Stand up a static site for acme/marketing-site and point acme.com at it\",\n  \"plan\": {\n    \"nodes\": [\n      { \"id\": \"build\",  \"workflow\": \"\u003Cbuild-static-site>\", \"inputs\": { \"repo\": \"acme/marketing-site\" } },\n      { \"id\": \"deploy\", \"workflow\": \"\u003Cdeploy-to-cdn>\",     \"inputs\": { \"artifact\": \"${build.output.artifact}\" } },\n      { \"id\": \"dns\",    \"workflow\": \"\u003Cpoint-domain>\",      \"inputs\": { \"domain\": \"acme.com\", \"target\": \"${deploy.output.endpoint}\" } }\n    ],\n    \"edges\": [\n      { \"from\": \"build\",  \"to\": \"deploy\" },\n      { \"from\": \"deploy\", \"to\": \"dns\" }\n    ]\n  }\n}\n","json","",[925,1187,1188,1197,1224,1239,1256,1333,1403,1491,1497,1511,1550,1587,1593,1599],{"__ignoreMap":1185},[1189,1190,1193],"span",{"class":1191,"line":1192},"line",1,[1189,1194,1196],{"class":1195},"sMK4o","{\n",[1189,1198,1200,1203,1206,1209,1212,1215,1219,1221],{"class":1191,"line":1199},2,[1189,1201,1202],{"class":1195},"  \"",[1189,1204,955],{"class":1205},"spNyl",[1189,1207,1208],{"class":1195},"\"",[1189,1210,1211],{"class":1195},":",[1189,1213,1214],{"class":1195}," \"",[1189,1216,1218],{"class":1217},"sfazB","Stand up a static site for acme/marketing-site and point acme.com at it",[1189,1220,1208],{"class":1195},[1189,1222,1223],{"class":1195},",\n",[1189,1225,1227,1229,1232,1234,1236],{"class":1191,"line":1226},3,[1189,1228,1202],{"class":1195},[1189,1230,1231],{"class":1205},"plan",[1189,1233,1208],{"class":1195},[1189,1235,1211],{"class":1195},[1189,1237,1238],{"class":1195}," {\n",[1189,1240,1242,1245,1249,1251,1253],{"class":1191,"line":1241},4,[1189,1243,1244],{"class":1195},"    \"",[1189,1246,1248],{"class":1247},"sBMFI","nodes",[1189,1250,1208],{"class":1195},[1189,1252,1211],{"class":1195},[1189,1254,1255],{"class":1195}," [\n",[1189,1257,1259,1262,1264,1268,1270,1272,1274,1277,1279,1282,1284,1286,1288,1290,1292,1295,1297,1299,1301,1304,1306,1308,1311,1313,1317,1319,1321,1323,1325,1327,1330],{"class":1191,"line":1258},5,[1189,1260,1261],{"class":1195},"      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],\n",[1189,1498,1500,1502,1505,1507,1509],{"class":1191,"line":1499},9,[1189,1501,1244],{"class":1195},[1189,1503,1504],{"class":1247},"edges",[1189,1506,1208],{"class":1195},[1189,1508,1211],{"class":1195},[1189,1510,1255],{"class":1195},[1189,1512,1514,1516,1518,1521,1523,1525,1527,1529,1531,1533,1535,1538,1540,1542,1544,1546,1548],{"class":1191,"line":1513},10,[1189,1515,1261],{"class":1195},[1189,1517,1214],{"class":1195},[1189,1519,1520],{"class":1266},"from",[1189,1522,1208],{"class":1195},[1189,1524,1211],{"class":1195},[1189,1526,1214],{"class":1195},[1189,1528,1276],{"class":1217},[1189,1530,1208],{"class":1195},[1189,1532,1281],{"class":1195},[1189,1534,1202],{"class":1195},[1189,1536,1537],{"class":1266},"to",[1189,1539,1208],{"class":1195},[1189,1541,1211],{"class":1195},[1189,1543,1214],{"class":1195},[1189,1545,1350],{"class":1217},[1189,1547,1208],{"class":1195},[1189,1549,1332],{"class":1195},[1189,1551,1553,1555,1557,1559,1561,1563,1565,1567,1569,1571,1573,1575,1577,1579,1581,1583,1585],{"class":1191,"line":1552},11,[1189,1554,1261],{"class":1195},[1189,1556,1214],{"class":1195},[1189,1558,1520],{"class":1266},[1189,1560,1208],{"class":1195},[1189,1562,1211],{"class":1195},[1189,1564,1214],{"class":1195},[1189,1566,1350],{"class":1217},[1189,1568,1208],{"class":1195},[1189,1570,1281],{"class":1195},[1189,1572,1214],{"class":1195},[1189,1574,1537],{"class":1266},[1189,1576,1208],{"class":1195},[1189,1578,1211],{"class":1195},[1189,1580,1214],{"class":1195},[1189,1582,1420],{"class":1217},[1189,1584,1208],{"class":1195},[1189,1586,1490],{"class":1195},[1189,1588,1590],{"class":1191,"line":1589},12,[1189,1591,1592],{"class":1195},"    ]\n",[1189,1594,1596],{"class":1191,"line":1595},13,[1189,1597,1598],{"class":1195},"  }\n",[1189,1600,1602],{"class":1191,"line":1601},14,[1189,1603,1604],{"class":1195},"}\n",[1606,1607,1608],"warning",{},[933,1609,1610,1611,1613],{},"Workflow names in angle brackets are placeholders. Real types come from the live catalog through ",[925,1612,1133],{},", never from this example.",[933,1615,1616],{},"The DAG is validated as a whole before anything runs: no cycles, every required input bound, every binding type-compatible with the producing step's output.",[1055,1618,1620],{"id":1619},"_4-execute-as-a-workflow","4. Execute as a workflow",[933,1622,1623,1624,1627,1628,1630,1631,1634,1635,1638,1639,1642],{},"Once assembled, the plan is just a run on the deterministic ",[937,1625,1626],{"href":54},"engine",": event-sourced state, per-run locks, async transitions, ",[937,1629,437],{"href":78}," traces, and step-level retry. ",[896,1632,1633],{},"Execution is deterministic."," The AI reasoned about ",[949,1636,1637],{},"which"," steps and ",[949,1640,1641],{},"how"," they connect; running them does not call the model again.",[885,1644,1646],{"id":1645},"dgi-assistants-and-mcp","DGI, assistants, and MCP",[933,1648,1649,1650,1653],{},"DGI and the MCP server are two ways into the ",[896,1651,1652],{},"same"," catalog:",[1098,1655,1656,1667],{},[1101,1657,1658],{},[1104,1659,1660,1662,1664],{},[1107,1661],{},[1107,1663,851],{},[1107,1665,1666],{},"Assistant over MCP",[1117,1668,1669,1687,1701,1712],{},[1104,1670,1671,1674,1680],{},[1122,1672,1673],{},"Driver",[1122,1675,1676,1677,1679],{},"A person (or agent) with a ",[949,1678,955],{}," in language",[1122,1681,1682,1683,1686],{},"An assistant with a ",[949,1684,1685],{},"task",", calling tools",[1104,1688,1689,1692,1695],{},[1122,1690,1691],{},"Selection",[1122,1693,1694],{},"Reasons over schemas to assemble a multi-step plan",[1122,1696,1697,1698],{},"Inspects one schema, then ",[925,1699,1700],{},"start_workflow",[1104,1702,1703,1706,1709],{},[1122,1704,1705],{},"Output",[1122,1707,1708],{},"A workflow DAG",[1122,1710,1711],{},"One or more individual runs",[1104,1713,1714,1717,1720],{},[1122,1715,1716],{},"Shared substrate",[1122,1718,1719],{},"Same registry, schemas, engine, Lumen traces",[1122,1721,1719],{},[933,1723,1724,1725,1727,1728,1731],{},"An assistant can use DGI as a planning tool (\"interpret this goal into a plan\") and then watch the resulting run over MCP. ",[937,1726,978],{"href":64}," runs DGI-produced plans as managed, budgeted, audited work. Because every selection is schema-grounded and every step is a registered workflow, the same governance, ",[937,1729,1730],{"href":83},"identity",", and observability apply no matter who pressed the button.",[1167,1733,1734],{},[933,1735,1736,1737,1740,1741,1744],{},"Plans are still ",[896,1738,1739],{},"connection-scoped",". DGI binds to provider ",[937,1742,1743],{"href":237},"connections","; it never embeds secrets into a plan or a prompt. Execution happens in your cloud.",[885,1746,1748],{"id":1747},"from-designed-plan-to-composition","From designed plan to composition",[933,1750,1751,1752,1755],{},"The first time DGI interprets a goal, it pays the full cost: interpretation, catalog search, schema inspection, assembly. Re-reasoning a recurring goal every time is wasteful and non-deterministic. The hybrid model closes this gap: a plan that proves good is ",[896,1753,1754],{},"compiled into a composition",", a single reusable, named, deterministic workflow type with its own schema.",[981,1757],{":edges":1758,":nodes":1759,"direction":1760},"[{\"from\":\"goal\",\"to\":\"dgi_plan\"},{\"from\":\"dgi_plan\",\"to\":\"vw\",\"label\":\"compile / promote\"},{\"from\":\"vw\",\"to\":\"start_wf\"},{\"from\":\"start_wf\",\"to\":\"engine_run\"}]","[{\"id\":\"goal\",\"label\":\"Goal\",\"kind\":\"start\"},{\"id\":\"dgi_plan\",\"label\":\"DGI plan / DAG\",\"kind\":\"ai\"},{\"id\":\"vw\",\"label\":\"Composition\",\"kind\":\"engine\"},{\"id\":\"start_wf\",\"label\":\"start_workflow, no model call\",\"kind\":\"engine\"},{\"id\":\"engine_run\",\"label\":\"Engine executes frozen DAG\",\"kind\":\"engine\"}]","LR",[933,1762,1763,1764,1767],{},"A finished composition runs as ",[925,1765,1766],{},"virtual.\u003Cuuid>@\u003Cversion>",", has a typed input schema, and runs with zero model inference. Design once with reasoning; run forever without it.",[885,1769,1771],{"id":1770},"current-capabilities","Current capabilities",[1098,1773,1774,1784],{},[1101,1775,1776],{},[1104,1777,1778,1781],{},[1107,1779,1780],{},"Capability",[1107,1782,1783],{},"Status",[1117,1785,1786,1794,1801,1809,1817,1824,1831,1839,1850,1868,1880],{},[1104,1787,1788,1791],{},[1122,1789,1790],{},"Interpret a goal or dialog into structured intent",[1122,1792,1793],{},"Available",[1104,1795,1796,1799],{},[1122,1797,1798],{},"Select capabilities against the org catalog",[1122,1800,1793],{},[1104,1802,1803,1806],{},[1122,1804,1805],{},"DAG assembly and layered validation (virtual engine)",[1122,1807,1808],{},"Available as a library; one-click DGI surface on the roadmap",[1104,1810,1811,1814],{},[1122,1812,1813],{},"Deterministic engine running registered workflows",[1122,1815,1816],{},"Available (shared with all workflows)",[1104,1818,1819,1822],{},[1122,1820,1821],{},"Clarifying-question dialog for underspecified goals",[1122,1823,1793],{},[1104,1825,1826,1829],{},[1122,1827,1828],{},"Driving and watching runs from an assistant over MCP",[1122,1830,1793],{},[1104,1832,1833,1836],{},[1122,1834,1835],{},"DGI auto-dispatching an assembled plan end to end",[1122,1837,1838],{},"Roadmap",[1104,1840,1841,1844],{},[1122,1842,1843],{},"Promote a designed plan into a composition and run it",[1122,1845,1846,1847],{},"Available to members: ",[925,1848,1849],{},"dgi.workflow.promote",[1104,1851,1852,1862],{},[1122,1853,1854,1855,1130,1858,1861],{},"Structured answers with cards (chat, ",[925,1856,1857],{},"dgi.chat.*",[925,1859,1860],{},"dgi.view.render",")",[1122,1863,1864,1865],{},"Available. See ",[937,1866,1867],{"href":864},"DGI interfaces",[1104,1869,1870,1876],{},[1122,1871,1872,1873,1861],{},"Living Surfaces (",[925,1874,1875],{},"dgi.surface.*",[1122,1877,1864,1878],{},[937,1879,846],{"href":847},[1104,1881,1882,1885],{},[1122,1883,1884],{},"Plan cost and latency estimation before execution",[1122,1886,1838],{},[885,1888,1890],{"id":1889},"ask-your-ai-assistant","Ask your AI assistant",[1180,1892,1898],{"className":1893,"code":1895,"filename":1896,"language":1897,"meta":1185},[1894],"language-text","Plan, without executing, how to deploy a static site from GitHub and point a domain at it. Use only workflow types that exist in my catalog and show each step's inputs.\n\nList the dgi.* workflow types in my org and describe what each one does.\n\nTurn this plan into a composition definition with explicit input mappings, validate it, and show me the errors.\n","prompts","text",[925,1899,1895],{"__ignoreMap":1185},[885,1901,1903],{"id":1902},"for-ai-agents","For AI agents",[1098,1905,1906,1916],{},[1101,1907,1908],{},[1104,1909,1910,1913],{},[1107,1911,1912],{},"Rule",[1107,1914,1915],{},"Detail",[1117,1917,1918,1929,1939,1951,1963],{},[1104,1919,1920,1923],{},[1122,1921,1922],{},"Ground selection in schemas",[1122,1924,1925,1926,1928],{},"Use ",[925,1927,1146],{}," for every candidate step. Bind inputs only to the goal, a connection, or a prior step's output.",[1104,1930,1931,1934],{},[1122,1932,1933],{},"Never invent types",[1122,1935,1936,1937,960],{},"Every step must come from ",[925,1938,1133],{},[1104,1940,1941,1944],{},[1122,1942,1943],{},"Prefer a plan over ad-hoc calls",[1122,1945,1946,1947,1950],{},"For multi-step goals, assemble a DAG (see ",[925,1948,1949],{},"concept://dag",") and validate it, then run.",[1104,1952,1953,1956],{},[1122,1954,1955],{},"Freeze what recurs",[1122,1957,1958,1959,1962],{},"Save a proven plan with ",[925,1960,1961],{},"composition.save"," so future runs need no reasoning.",[1104,1964,1965,1968],{},[1122,1966,1967],{},"Respect gates",[1122,1969,1970],{},"Prerequisites and Staff approvals apply to every step of a plan.",[885,1972,1974],{"id":1973},"where-to-go-next","Where to go next",[1026,1976,1977,1983,1990,1996],{},[1029,1978,1980],{"icon":1979,"title":123,"to":124},"i-lucide-hammer",[933,1981,1982],{},"Hands-on: from a goal to a running plan.",[1029,1984,1987],{"icon":1985,"title":1986,"to":129},"i-lucide-combine","Virtual workflows",[933,1988,1989],{},"How designed plans become reusable compositions.",[1029,1991,1993],{"icon":198,"title":1992,"to":196},"Hybrid execution model",[933,1994,1995],{},"Why AI design and deterministic execution are split.",[1029,1997,1999],{"icon":44,"title":1998,"to":42},"Connect an AI assistant",[933,2000,2001],{},"The MCP loop DGI shares with external assistants.",[2003,2004,2005],"style",{},"html pre.shiki code .sMK4o, html code.shiki .sMK4o{--shiki-light:#39ADB5;--shiki-default:#89DDFF;--shiki-dark:#89DDFF}html pre.shiki code .spNyl, html code.shiki .spNyl{--shiki-light:#9C3EDA;--shiki-default:#C792EA;--shiki-dark:#C792EA}html pre.shiki code .sfazB, html code.shiki .sfazB{--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D}html pre.shiki code .sBMFI, html code.shiki .sBMFI{--shiki-light:#E2931D;--shiki-default:#FFCB6B;--shiki-dark:#FFCB6B}html pre.shiki code .sbssI, html code.shiki .sbssI{--shiki-light:#F76D47;--shiki-default:#F78C6C;--shiki-dark:#F78C6C}html pre.shiki code .swJcz, html code.shiki .swJcz{--shiki-light:#E53935;--shiki-default:#F07178;--shiki-dark:#F07178}html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":1185,"searchDepth":1192,"depth":1199,"links":2007},[2008,2009,2010,2016,2017,2018,2019,2020,2021],{"id":887,"depth":1199,"text":888},{"id":963,"depth":1199,"text":964},{"id":1023,"depth":1199,"text":1024,"children":2011},[2012,2013,2014,2015],{"id":1057,"depth":1226,"text":1058},{"id":1092,"depth":1226,"text":1093},{"id":1174,"depth":1226,"text":1175},{"id":1619,"depth":1226,"text":1620},{"id":1645,"depth":1199,"text":1646},{"id":1747,"depth":1199,"text":1748},{"id":1770,"depth":1199,"text":1771},{"id":1889,"depth":1199,"text":1890},{"id":1902,"depth":1199,"text":1903},{"id":1973,"depth":1199,"text":1974},"How Orkestia turns a natural-language goal into a typed, executable workflow plan, how that plan compiles into a reusable deterministic composition, and how DGI relates to assistants over MCP","md",null,{},{"icon":61},{"title":58,"description":2022},"OrPv4Q8wDfL0LwEe1Ml6rBF68bTOaC0bS-oJP69EtFU",[2030,2032],{"title":53,"path":54,"stem":55,"description":2031,"icon":56,"children":-1},"The execution model in depth. Workflow types versus runs, the event-sourced state machine, DAGs, how an assistant drives it over MCP, and compositions that compile into deterministic engine config",{"title":63,"path":64,"stem":65,"description":2033,"icon":66,"children":-1},"How Orkestia Staff turns a fleet of autonomous AI agents into a scoped, auditable organization with roles, approval gates, and human-in-the-loop oversight, enforced inside the workflow engine",1790354043230]