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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":195,"body":881,"description":2468,"extension":2469,"links":2470,"meta":2471,"navigation":2472,"path":196,"seo":2473,"stem":197,"__hash__":2474},"docs/06.advanced/2.hybrid-execution-model.md",{"type":882,"value":883,"toc":2454},"minimark",[884,889,898,925,936,951,955,962,965,1054,1066,1070,1075,1078,1083,1101,1108,1645,1662,1665,1706,1716,1720,1727,1789,1792,1882,1889,2209,2216,2226,2230,2244,2247,2270,2274,2284,2301,2308,2328,2332,2350,2360,2364,2375,2379,2410,2414,2450],[885,886,888],"h2",{"id":887},"tldr","TL;DR",[890,891,892,893,897],"p",{},"Orkestia's flagship execution model separates ",[894,895,896],"strong",{},"two jobs that most \"AI workflow\" tools conflate",":",[899,900,901,919],"ol",{},[902,903,904,907,908,912,913,915,916,918],"li",{},[894,905,906],{},"Design"," — figuring out ",[909,910,911],"em",{},"what"," steps to run, in ",[909,914,911],{}," order, with ",[909,917,911],{}," inputs. This is open-ended, benefits from reasoning, and is where AI (DGI and agents) earns its keep.",[902,920,921,924],{},[894,922,923],{},"Execution"," — actually running those steps reliably, every time, with locking, retries, observability, and an audit trail. This wants determinism, not creativity.",[890,926,927,928,931,932,935],{},"The hybrid model uses AI for design, then ",[894,929,930],{},"compiles the resulting plan into a deterministic virtual workflow (a composition)"," that the engine runs with ",[894,933,934],{},"no LLM in the hot path",". You explore once with AI, capture the result as a reusable composition, and from then on it runs like any other DAG: byte-stable, cheap, and fully auditable.",[937,938,939],"note",{},[890,940,941,942,946,947,950],{},"\"Virtual workflow\" and \"composition\" are the same thing in this document: an AI-authored (or tool-authored) DAG that has been validated and compiled into engine-ready config. See ",[943,944,945],"a",{"href":54},"Concepts → Workflows"," and the ",[943,948,949],{"href":129},"Virtual Workflows guide",".",[885,952,954],{"id":953},"why-split-design-from-execution","Why split design from execution",[890,956,957,958,961],{},"An LLM in the execution path is a liability: it is non-deterministic, slow, expensive per invocation, and hard to audit (\"why did it do ",[909,959,960],{},"that"," this time?\"). But an LLM is also the best tool we have for turning a vague human goal into a concrete plan against a large, changing catalog of capabilities.",[890,963,964],{},"Orkestia resolves the tension by letting the model operate only where its strengths apply:",[966,967,968,984],"table",{},[969,970,971],"thead",{},[972,973,974,978,981],"tr",{},[975,976,977],"th",{},"Concern",[975,979,980],{},"AI design phase",[975,982,983],{},"Deterministic execution phase",[985,986,987,999,1010,1021,1032,1043],"tbody",{},[972,988,989,993,996],{},[990,991,992],"td",{},"Decides step order / inputs",[990,994,995],{},"Yes (reasoning)",[990,997,998],{},"No — fixed by the compiled DAG",[972,1000,1001,1004,1007],{},[990,1002,1003],{},"Cost per run",[990,1005,1006],{},"High (token cost)",[990,1008,1009],{},"Near-zero (no model call)",[972,1011,1012,1015,1018],{},[990,1013,1014],{},"Reproducibility",[990,1016,1017],{},"Low (re-plans each time)",[990,1019,1020],{},"Byte-stable given same inputs",[972,1022,1023,1026,1029],{},[990,1024,1025],{},"Latency",[990,1027,1028],{},"Reasoning + tool roundtrips",[990,1030,1031],{},"Engine dispatch only",[972,1033,1034,1037,1040],{},[990,1035,1036],{},"Auditability",[990,1038,1039],{},"Plan is the artifact",[990,1041,1042],{},"Full event-sourced history",[972,1044,1045,1048,1051],{},[990,1046,1047],{},"Best for",[990,1049,1050],{},"Novel / one-off / ambiguous goals",[990,1052,1053],{},"Repeated, known, high-volume flows",[890,1055,1056,1057,1065],{},"The key insight from the platform's internal substrate: the compiler emits a dict that ",[894,1058,1059,1060,1064],{},"matches the real engine's ",[1061,1062,1063],"code",{},"DAGWorkflow"," format exactly — there is no runtime marker",". The engine does not know or care that a config was authored by an AI. Once compiled, a virtual workflow is indistinguishable from a hand-written one, which is precisely what makes it trustworthy.",[885,1067,1069],{"id":1068},"the-lifecycle-explore-capture-run","The lifecycle: explore → capture → run",[1071,1072],"dag-diagram",{":edges":1073,":nodes":1074},"[{\"from\":\"A\",\"to\":\"B\"},{\"from\":\"B\",\"to\":\"C\",\"label\":\"discovers capabilities over MCP\"},{\"from\":\"B\",\"to\":\"D\"},{\"from\":\"D\",\"to\":\"E\"},{\"from\":\"E\",\"to\":\"F\",\"label\":\"structure / references / compatibility\"},{\"from\":\"E\",\"to\":\"B\",\"label\":\"typed error\"},{\"from\":\"F\",\"to\":\"G\"},{\"from\":\"G\",\"to\":\"H\"},{\"from\":\"H\",\"to\":\"I\"},{\"from\":\"G\",\"to\":\"H\",\"label\":\"reusable composition\",\"dashed\":true}]","[{\"id\":\"A\",\"label\":\"Human goal / intent\",\"kind\":\"start\"},{\"id\":\"B\",\"label\":\"DGI / agent reasoning\",\"kind\":\"ai\"},{\"id\":\"C\",\"label\":\"Live workflow catalog\",\"kind\":\"data\"},{\"id\":\"D\",\"label\":\"VirtualWorkflow plan\",\"sub\":\"layers - steps - input mappings\"},{\"id\":\"E\",\"label\":\"Validate\"},{\"id\":\"F\",\"label\":\"compile_workflow\",\"kind\":\"engine\"},{\"id\":\"G\",\"label\":\"DAGWorkflow JSON\",\"sub\":\"engine-ready, no LLM marker\",\"kind\":\"data\"},{\"id\":\"H\",\"label\":\"Workflow engine\",\"sub\":\"deterministic run\",\"kind\":\"engine\"},{\"id\":\"I\",\"label\":\"Event-sourced history + Lumen observability\",\"kind\":\"terminal\"}]",[890,1076,1077],{},"The three phases:",[1079,1080,1082],"h3",{"id":1081},"_1-explore-with-ai","1. Explore with AI",[890,1084,1085,1086,1088,1089,1092,1093,1096,1097,1100],{},"A human expresses a goal. ",[894,1087,851],{}," (the Dialog Generative Interface) or an autonomous ",[894,1090,1091],{},"agent"," reasons about it, discovering available capabilities over ",[894,1094,1095],{},"MCP"," rather than against a static, hand-maintained list. The catalog is ",[909,1098,1099],{},"whatever is installed and importable"," — so the model plans against ground truth, not a stale snapshot.",[890,1102,1103,1104,1107],{},"The model emits a plan in the VirtualWorkflow wire format: layers of steps, where every step references a ",[1061,1105,1106],{},"{provider}.{service}.{operation}"," workflow type and every input is an explicit mapping with a known source.",[1109,1110,1115],"pre",{"className":1111,"code":1112,"language":1113,"meta":1114,"style":1114},"language-json shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","{\n  \"name\": \"provision_and_tag_bucket\",\n  \"layers\": [\n    {\n      \"name\": \"create\",\n      \"steps\": [\n        {\n          \"name\": \"s1\",\n          \"workflow_type\": \"aws.s3.create_bucket\",\n          \"input_mapping\": {\n            \"name\": { \"source\": \"input\", \"field_name\": \"bucket_name\" }\n          }\n        }\n      ]\n    },\n    {\n      \"name\": 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[{",[1118,1570,1143],{"class":1124},[1118,1572,1574],{"class":1573},"sfNiH","Key",[1118,1576,1138],{"class":1124},[1118,1578,897],{"class":1124},[1118,1580,1143],{"class":1124},[1118,1582,1583],{"class":1146},"env",[1118,1585,1138],{"class":1124},[1118,1587,1309],{"class":1124},[1118,1589,1143],{"class":1124},[1118,1591,1592],{"class":1573},"Value",[1118,1594,1138],{"class":1124},[1118,1596,897],{"class":1124},[1118,1598,1143],{"class":1124},[1118,1600,1601],{"class":1146},"demo",[1118,1603,1138],{"class":1124},[1118,1605,1606],{"class":1124}," }]",[1118,1608,1609],{"class":1124}," }",[1118,1611,1328],{"class":1124},[1118,1613,1615],{"class":1120,"line":1614},25,[1118,1616,1334],{"class":1124},[1118,1618,1620],{"class":1120,"line":1619},26,[1118,1621,1340],{"class":1124},[1118,1623,1625],{"class":1120,"line":1624},27,[1118,1626,1346],{"class":1124},[1118,1628,1630],{"class":1120,"line":1629},28,[1118,1631,1632],{"class":1124},"    }\n",[1118,1634,1636],{"class":1120,"line":1635},29,[1118,1637,1638],{"class":1124},"  ]\n",[1118,1640,1642],{"class":1120,"line":1641},30,[1118,1643,1644],{"class":1124},"}\n",[937,1646,1647],{},[890,1648,1649,1650,1652,1653,946,1659,950],{},"Workflow type names like ",[1061,1651,1254],{}," are illustrative. The authoritative, per-workflow catalog lives at ",[943,1654,1658],{"href":1655,"rel":1656},"https://reference.orkestia.dev",[1657],"nofollow","reference.orkestia.dev",[943,1660,1661],{"href":165},"Workflow Types Registry",[890,1663,1664],{},"Input mappings come from exactly three sources, with no implicit global scope and no runtime string templating:",[966,1666,1667,1677],{},[969,1668,1669],{},[972,1670,1671,1674],{},[975,1672,1673],{},"Source",[975,1675,1676],{},"Meaning",[985,1678,1679,1688,1697],{},[972,1680,1681,1685],{},[990,1682,1683],{},[1061,1684,1304],{},[990,1686,1687],{},"A field from the workflow's own input",[972,1689,1690,1694],{},[990,1691,1692],{},[1061,1693,1476],{},[990,1695,1696],{},"An output field of a prior step",[972,1698,1699,1703],{},[990,1700,1701],{},[1061,1702,1540],{},[990,1704,1705],{},"A hardcoded literal",[890,1707,1708,1709,1712,1713,950],{},"This explicitness is what makes the plan ",[909,1710,1711],{},"compilable"," — and later, ",[909,1714,1715],{},"auditable",[1079,1717,1719],{"id":1718},"_2-capture-as-a-composition","2. Capture as a composition",[890,1721,1722,1723,1726],{},"Before anything runs, the plan goes through ",[894,1724,1725],{},"three-phase validation",", each phase short-circuiting with a typed error so the AI can self-correct in a loop:",[966,1728,1729,1742],{},[969,1730,1731],{},[972,1732,1733,1736,1739],{},[975,1734,1735],{},"Phase",[975,1737,1738],{},"Checks",[975,1740,1741],{},"Catches",[985,1743,1744,1757,1773],{},[972,1745,1746,1751,1754],{},[990,1747,1748],{},[894,1749,1750],{},"Structure",[990,1752,1753],{},"DAG acyclicity, unique step IDs",[990,1755,1756],{},"malformed plans",[972,1758,1759,1764,1770],{},[990,1760,1761],{},[894,1762,1763],{},"References",[990,1765,1766,1767,1769],{},"every ",[1061,1768,1106],{}," exists in the live catalog",[990,1771,1772],{},"hallucinated / typo'd workflow types",[972,1774,1775,1780,1786],{},[990,1776,1777],{},[894,1778,1779],{},"Compatibility",[990,1781,1766,1782,1785],{},[1061,1783,1784],{},"InputMapping"," resolves to a compatible source field & type",[990,1787,1788],{},"shape drift, wrong wiring",[890,1790,1791],{},"A failure is structured data, not a stack trace in a string — designed to be fed straight back into the model:",[1109,1793,1795],{"className":1111,"code":1794,"language":1113,"meta":1114,"style":1114},"{\n  \"phase\": \"references\",\n  \"step_id\": \"s2\",\n  \"workflow_type\": \"aws.s3.put_bucket_taging\",\n  \"reason\": \"unknown workflow type; did you mean 'aws.s3.put_bucket_tagging'?\"\n}\n",[1061,1796,1797,1801,1821,1840,1859,1878],{"__ignoreMap":1114},[1118,1798,1799],{"class":1120,"line":1121},[1118,1800,1125],{"class":1124},[1118,1802,1803,1805,1808,1810,1812,1814,1817,1819],{"class":1120,"line":1128},[1118,1804,1131],{"class":1124},[1118,1806,1807],{"class":1134},"phase",[1118,1809,1138],{"class":1124},[1118,1811,897],{"class":1124},[1118,1813,1143],{"class":1124},[1118,1815,1816],{"class":1146},"references",[1118,1818,1138],{"class":1124},[1118,1820,1152],{"class":1124},[1118,1822,1823,1825,1828,1830,1832,1834,1836,1838],{"class":1120,"line":1155},[1118,1824,1131],{"class":1124},[1118,1826,1827],{"class":1134},"step_id",[1118,1829,1138],{"class":1124},[1118,1831,897],{"class":1124},[1118,1833,1143],{"class":1124},[1118,1835,1411],{"class":1146},[1118,1837,1138],{"class":1124},[1118,1839,1152],{"class":1124},[1118,1841,1842,1844,1846,1848,1850,1852,1855,1857],{"class":1120,"line":1170},[1118,1843,1131],{"class":1124},[1118,1845,1245],{"class":1134},[1118,1847,1138],{"class":1124},[1118,1849,897],{"class":1124},[1118,1851,1143],{"class":1124},[1118,1853,1854],{"class":1146},"aws.s3.put_bucket_taging",[1118,1856,1138],{"class":1124},[1118,1858,1152],{"class":1124},[1118,1860,1861,1863,1866,1868,1870,1872,1875],{"class":1120,"line":1176},[1118,1862,1131],{"class":1124},[1118,1864,1865],{"class":1134},"reason",[1118,1867,1138],{"class":1124},[1118,1869,897],{"class":1124},[1118,1871,1143],{"class":1124},[1118,1873,1874],{"class":1146},"unknown workflow type; did you mean 'aws.s3.put_bucket_tagging'?",[1118,1876,1877],{"class":1124},"\"\n",[1118,1879,1880],{"class":1120,"line":1198},[1118,1881,1644],{"class":1124},[890,1883,1884,1885,1888],{},"Once validated, ",[1061,1886,1887],{},"compile_workflow"," emits engine-ready JSON:",[1109,1890,1892],{"className":1111,"code":1891,"language":1113,"meta":1114,"style":1114},"{\n  \"id\": \"provision_and_tag_bucket\",\n  \"layers\": [\n    { \"steps\": [ { \"id\": \"s1\", \"workflow\": \"aws.s3.create_bucket\",\n                   \"inputs\": { \"name\": {\"from\": \"input.bucket_name\"} } } ] },\n    { \"steps\": [ { \"id\": \"s2\", \"workflow\": \"aws.s3.put_bucket_tagging\",\n                   \"inputs\": {\n                     \"bucket\":  {\"from\": \"step.s1.bucket_name\"},\n                     \"tagging\": {\"value\": { \"TagSet\": [{ \"Key\": \"env\", \"Value\": \"demo\" }] }}\n                   } } ] }\n  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                    \"",[1118,2098,1456],{"class":1282},[1118,2100,1138],{"class":1124},[1118,2102,897],{"class":1124},[1118,2104,1463],{"class":1124},[1118,2106,1138],{"class":1124},[1118,2108,2008],{"class":1294},[1118,2110,1138],{"class":1124},[1118,2112,897],{"class":1124},[1118,2114,1143],{"class":1124},[1118,2116,2117],{"class":1146},"step.s1.bucket_name",[1118,2119,1138],{"class":1124},[1118,2121,2122],{"class":1124},"},\n",[1118,2124,2125,2127,2129,2131,2133,2135,2137,2139,2141,2143,2145,2147,2149,2151,2153,2155,2157,2159,2161,2163,2165,2167,2169,2171,2173,2175,2177,2179,2181,2183,2185,2187],{"class":1120,"line":1240},[1118,2126,2096],{"class":1124},[1118,2128,1521],{"class":1282},[1118,2130,1138],{"class":1124},[1118,2132,897],{"class":1124},[1118,2134,1289],{"class":1124},[1118,2136,1138],{"class":1124},[1118,2138,1549],{"class":1294},[1118,2140,1138],{"class":1124},[1118,2142,897],{"class":1124},[1118,2144,1289],{"class":1124},[1118,2146,1143],{"class":1124},[1118,2148,1561],{"class":1560},[1118,2150,1138],{"class":1124},[1118,2152,897],{"class":1124},[1118,2154,1568],{"class":1124},[1118,2156,1143],{"class":1124},[1118,2158,1574],{"class":1573},[1118,2160,1138],{"class":1124},[1118,2162,897],{"class":1124},[1118,2164,1143],{"class":1124},[1118,2166,1583],{"class":1146},[1118,2168,1138],{"class":1124},[1118,2170,1309],{"class":1124},[1118,2172,1143],{"class":1124},[1118,2174,1592],{"class":1573},[1118,2176,1138],{"class":1124},[1118,2178,897],{"class":1124},[1118,2180,1143],{"class":1124},[1118,2182,1601],{"class":1146},[1118,2184,1138],{"class":1124},[1118,2186,1606],{"class":1124},[1118,2188,2189],{"class":1124}," }}\n",[1118,2191,2192,2195,2197,2199],{"class":1120,"line":1261},[1118,2193,2194],{"class":1124},"                   }",[1118,2196,1609],{"class":1124},[1118,2198,2029],{"class":1124},[1118,2200,1328],{"class":1124},[1118,2202,2203],{"class":1120,"line":1276},[1118,2204,1638],{"class":1124},[1118,2206,2207],{"class":1120,"line":1331},[1118,2208,1644],{"class":1124},[890,2210,2211,2212,2215],{},"This JSON ",[894,2213,2214],{},"is"," the composition. It's the durable artifact you version, diff, review, and reuse.",[2217,2218,2219],"tip",{},[890,2220,2221,2222,2225],{},"Because compilation is deterministic — ",[909,2223,2224],{},"same plan + same installed catalog → byte-stable JSON"," — you can diff two AI-authored plans the way you'd diff source code. That makes AI output reviewable in a pull request rather than trusted on faith.",[1079,2227,2229],{"id":2228},"_3-run-deterministically","3. Run deterministically",[890,2231,2232,2233,2236,2237,2240,2241,2243],{},"From here the AI is gone. The compiled DAG is dispatched to the workflow engine, which runs it like any other workflow: PG advisory locks for concurrency, the middleware pipeline, retries, and an ",[894,2234,2235],{},"event-sourced history"," that records every transition. The ",[1061,2238,2239],{},"workflow.transition"," events flow through Kafka to the consumer (the canonical async path), and ",[943,2242,437],{"href":78}," captures observability and the audit trail.",[890,2245,2246],{},"No tokens are spent. No model is queried. The run at 3am behaves exactly like the run at noon.",[2248,2249,2250],"callout",{"icon":16},[890,2251,2252,2253,2256,2257,2260,2261,2264,2265,2267,2268,950],{},"This is also where Orkestia's ",[894,2254,2255],{},"Zero Code Custody"," posture holds: the deterministic run executes against the ",[909,2258,2259],{},"customer's own cloud accounts"," via ",[943,2262,2263],{"href":73},"runners",". Orkestia orchestrates state and observability; it never takes custody of customer code or data. See ",[943,2266,138],{"href":139}," and ",[943,2269,222],{"href":223},[885,2271,2273],{"id":2272},"when-to-stay-ai-driven-vs-when-to-compile","When to stay AI-driven vs. when to compile",[890,2275,2276,2277,2267,2280,2283],{},"Compiling is not always the right move. The decision turns on how ",[909,2278,2279],{},"stable",[909,2281,2282],{},"repeated"," the task is.",[2285,2286,2287,2294],"card-group",{},[2288,2289,2291],"card",{"icon":61,"title":2290},"Stay AI-driven",[890,2292,2293],{},"The goal is novel, ambiguous, or one-off. Inputs vary structurally each time. A human is in the loop and wants conversational iteration. The catalog or environment is still changing under you. Cost-per-run is acceptable because volume is low.",[2288,2295,2298],{"icon":2296,"title":2297},"i-lucide-package-check","Compile to a composition",[890,2299,2300],{},"The same shape of task recurs. You need reproducibility and a stable audit trail. Volume makes per-run LLM cost or latency unacceptable. You want the flow reviewable in version control and runnable by non-AI callers (schedules, webhooks, end-users).",[890,2302,2303,2304,2307],{},"A useful mental model: ",[894,2305,2306],{},"AI authors the first run; the composition owns every run after."," You let the model explore until a plan is good, then \"freeze\" it into a deterministic workflow. If requirements change later, you go back to the AI, regenerate, re-validate, and ship a new compiled version — the same way you'd edit and re-merge code.",[2309,2310,2311],"warning",{},[890,2312,2313,2316,2317,2320,2321,2324,2325,2327],{},[894,2314,2315],{},"Catalog drift is the sharp edge."," The catalog is a ",[909,2318,2319],{},"snapshot at compile time",". If a workflow type is added after you compile, that composition won't see it; if a type is removed after compile, the JSON still references it and the run fails at engine dispatch — a ",[909,2322,2323],{},"runtime"," \"unknown workflow\" error, not a validation error. Mitigation: compile in (or against) the same environment that will dispatch, and pin library versions across authoring and runtime. See the ",[943,2326,949],{"href":129}," for the recommended setup.",[885,2329,2331],{"id":2330},"how-ai-reaches-the-catalog","How AI reaches the catalog",[890,2333,2334,2335,2337,2338,2341,2342,2345,2346,2349],{},"The design phase doesn't guess at capabilities — it discovers them. Agents and DGI talk to the engine over ",[894,2336,1095],{}," (",[943,2339,2340],{"href":170},"MCP integration","): listing workflow namespaces and types, fetching schemas, checking prerequisites, then starting and watching runs. The same surface that an AI uses to ",[909,2343,2344],{},"discover"," capabilities is the one it uses to ",[909,2347,2348],{},"run"," a compiled composition, so there's a single, consistent contract from exploration through execution.",[890,2351,2352,2353,2356,2357,950],{},"The ",[1061,2354,2355],{},"dgi.*"," workflow family makes the design phase itself a first-class, discoverable surface — construction, conversion, template resolution, and execution are workflow types the engine can run, not hidden internal calls. See ",[943,2358,2359],{"href":59},"Concepts → DGI",[885,2361,2363],{"id":2362},"governance-fits-naturally","Governance fits naturally",[890,2365,2366,2367,2370,2371,2374],{},"Because compilation produces a reviewable artifact and execution produces an event-sourced history, the hybrid model slots cleanly into ",[943,2368,2369],{"href":64},"Staff governance",": a fleet of AI agents can ",[909,2372,2373],{},"propose"," compositions, while approval gates decide which ones are allowed to compile and run. The deterministic phase is where oversight has teeth — you approve a frozen plan, not a probability distribution over future model behavior.",[885,2376,2378],{"id":2377},"notes-limitations","Notes & limitations",[937,2380,2381,2384],{},[890,2382,2383],{},"A few things to keep in mind when building on the hybrid model:",[2385,2386,2387,2393,2396,2403],"ul",{},[902,2388,2389,2390,950],{},"The VirtualWorkflow input format may still evolve; the ",[894,2391,2392],{},"wire JSON is the more stable contract",[902,2394,2395],{},"There is no cross-version catalog arbitration today — if two library versions coexist, import resolution order wins.",[902,2397,2398,2399,2402],{},"The compiler is ",[894,2400,2401],{},"not a runtime",": it cannot step, pause, resume, or observe a run. Use the engine for that.",[902,2404,2405,2406,2409],{},"Per-workflow specifics (exact names, inputs, limits) come from ",[943,2407,1658],{"href":1655,"rel":2408},[1657],", not inferred from examples here.",[885,2411,2413],{"id":2412},"where-to-go-next","Where to go next",[2285,2415,2416,2421,2426,2432,2437,2443],{},[2288,2417,2418],{"icon":56,"title":949,"to":129},[890,2419,2420],{},"Hands-on: author, validate, and compile a composition end to end.",[2288,2422,2423],{"icon":11,"title":123,"to":124},[890,2424,2425],{},"Drive the design phase conversationally with the Dialog Generative Interface.",[2288,2427,2429],{"icon":155,"title":2428,"to":54},"Workflows concept",[890,2430,2431],{},"The engine, the 3-state pattern, and DAG execution that compositions run on.",[2288,2433,2434],{"icon":181,"title":2340,"to":170},[890,2435,2436],{},"How agents discover and run capabilities over MCP.",[2288,2438,2440],{"icon":80,"title":2439,"to":201},"Drift detection & self-healing",[890,2441,2442],{},"The deterministic counterpart for infrastructure state.",[2288,2444,2447],{"icon":2445,"title":2446,"to":78},"i-lucide-telescope","Lumen observability",[890,2448,2449],{},"The audit trail and observability behind every deterministic run.",[2451,2452,2453],"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 pre.shiki code .su27w, html code.shiki .su27w{--shiki-light:#916B53;--shiki-default:#916B53;--shiki-dark:#916B53}html pre.shiki code .s2Zo4, html code.shiki .s2Zo4{--shiki-light:#6182B8;--shiki-default:#82AAFF;--shiki-dark:#82AAFF}html pre.shiki code .sfNiH, html code.shiki .sfNiH{--shiki-light:#FF5370;--shiki-default:#FF9CAC;--shiki-dark:#FF9CAC}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":1114,"searchDepth":1121,"depth":1128,"links":2455},[2456,2457,2458,2463,2464,2465,2466,2467],{"id":887,"depth":1128,"text":888},{"id":953,"depth":1128,"text":954},{"id":1068,"depth":1128,"text":1069,"children":2459},[2460,2461,2462],{"id":1081,"depth":1155,"text":1082},{"id":1718,"depth":1155,"text":1719},{"id":2228,"depth":1155,"text":2229},{"id":2272,"depth":1128,"text":2273},{"id":2330,"depth":1128,"text":2331},{"id":2362,"depth":1128,"text":2363},{"id":2377,"depth":1128,"text":2378},{"id":2412,"depth":1128,"text":2413},"How Orkestia lets AI design workflows at runtime, then compiles those plans into deterministic virtual workflows that run repeatably, cheaply, and auditably","md",null,{},{"icon":198},{"title":195,"description":2468},"giJr3K1_HsVbwsM4xaUnk2i4yDVU_vvuGPjrZlq2aSo",[2476,2478],{"title":188,"path":189,"stem":190,"description":2477,"icon":193,"children":-1},"Deep dives into the mechanisms that make Orkestia reliable, governed, and efficient at scale",{"title":200,"path":201,"stem":202,"description":2479,"icon":203,"children":-1},"How Orkestia reconciles desired and actual state for runners and network infrastructure in your cloud, computes readiness verdicts, and decides what to heal automatically versus surface for approval",1790354041705]