fix(agent): continue truncated model turns

apps/agent-worker/README.md

@@ -87,6 +87,10 @@ the Daytona adapter normalizes the provider's structured `503` response to an in
 then the lifecycle verifies the active-run lease and canonical volume mount before replacing the
 stopped container on the same isolated workspace-volume subpath. This preserves user files while
 avoiding an indefinite dependency on one unhealthy runner.
+Provider output-length termination is nonterminal: the Workflow checkpoints the partial model turn,
+adds an internal continuation message, and resumes from that durable state. A tool-free turn completes
+the run only when the model reports an actual stop; content filtering, provider errors, and invalid
+terminal reasons fail explicitly instead of publishing incomplete work as success.
 There is no application step, token, duration, or cost ceiling; semantic completion ends the loop,
 while per-operation timeouts and the platform Workflow limit remain operational safeguards.
 The Worker pins Cloudflare's paid-plan maximum subrequest allowance because external provider,

apps/agent-worker/src/durable-objects/agent-run-workflow-runtime.ts

@@ -1,5 +1,6 @@
 import {
   executeGeneralAgentTool,
+  GeneralAgentFinishReasonSchema,
   type GeneralAgentToolCall,
   generateGeneralAgentStep,
 } from "@cheatcode/agent-core";
@@ -87,7 +88,7 @@ export const WorkflowModelStepResultSchema = z.strictObject({
   input: StartRunInputSchema,
   logicalModelId: LogicalModelIdSchema,
   step: z.strictObject({
-    finishReason: z.string(),
+    finishReason: GeneralAgentFinishReasonSchema,
     responseMessages: z.array(WorkflowJsonValueSchema),
     text: z.string(),
     toolCalls: z.array(WorkflowToolCallSchema),

apps/agent-worker/src/durable-objects/agent-run-workflow.ts

@@ -1,6 +1,7 @@
 import { WorkflowEntrypoint, type WorkflowEvent, type WorkflowStep } from "cloudflare:workers";
 import { NonRetryableError } from "cloudflare:workflows";
 import {
+  APIError,
   createLogger,
   emitErrorEvent,
   emitUserEvent,
@@ -57,6 +58,20 @@ const STATE_STEP = stepConfig("2 minutes", 5);
 const CLEANUP_STEP = stepConfig("5 minutes", 5);
 const FAILURE_STEP = stepConfig("2 minutes", AGENT_RUN_WORKFLOW_FAILURE_RETRY_LIMIT);
 const JsonValueSchema = z.json();
+const MODEL_LENGTH_CONTINUATION_MESSAGE: ModelMessage = {
+  role: "user",
+  content: [
+    {
+      type: "text",
+      text: [
+        "The provider ended your previous turn at its per-response output limit.",
+        "Continue the unfinished work from the current state without repeating completed work.",
+        "If a tool call was interrupted, split the remaining operation into smaller complete tool calls.",
+        "Do not claim completion until the requested outcome has been verified.",
+      ].join(" "),
+    },
+  ],
+};
 
 interface AgentRunWorkflowEnv extends AgentRunEnv, AgentRunWorkflowBindings {
   AGENT_RUN: DurableObjectNamespace<AgentRun>;
@@ -185,6 +200,11 @@ async function runAgentLoop(
       stepIndex,
     );
     if (model.step.toolCalls.length === 0) {
+      if (model.step.finishReason === "length") {
+        state = continueTruncatedModelTurn(state);
+        continue;
+      }
+      requireSemanticCompletion(model.step.finishReason);
       await publishClosingBackstopIfNeeded(env, workflowStep, workflowInstanceId, payload, state);
       return;
     }
@@ -219,6 +239,23 @@ async function runAgentLoop(
   }
 }
 
+function continueTruncatedModelTurn(state: WorkflowAgentState): WorkflowAgentState {
+  return WorkflowAgentStateSchema.parse({
+    ...state,
+    messages: [...state.messages, workflowJsonValue(MODEL_LENGTH_CONTINUATION_MESSAGE)],
+  });
+}
+
+function requireSemanticCompletion(
+  finishReason: WorkflowModelStepResult["step"]["finishReason"],
+): void {
+  if (finishReason === "stop") return;
+  throw new APIError(502, "upstream_llm_failed", "The model stopped before completing the run.", {
+    details: { finishReason },
+    retriable: finishReason !== "content-filter",
+  });
+}
+
 async function executeModelWorkflowStep(input: {
   env: AgentRunWorkflowEnv;
   payload: AgentRunWorkflowPayload;

packages/agent-core/README.md

@@ -7,6 +7,7 @@ Mastra agents, tool registry, and workflow entrypoints.
 - `createCodeRequestContext`
 - `generateGeneralAgentStep` and `executeGeneralAgentTool` for Cloudflare
   Workflow-owned, step-granular agent execution
+- `GeneralAgentFinishReasonSchema`, the validated model-turn completion contract
 - runtime credential and model contracts consumed by `agent-worker`
 
 The tool and agent registries are statically constrained by the lightweight

packages/agent-core/src/index.ts

@@ -11,6 +11,7 @@ export type {
 } from "./mastra/composio-context";
 export {
   executeGeneralAgentTool,
+  GeneralAgentFinishReasonSchema,
   type GeneralAgentToolCall,
   generateGeneralAgentStep,
 } from "./mastra/durable-agent-step";

packages/agent-core/src/mastra/durable-agent-step.ts

@@ -4,14 +4,25 @@ import { z } from "zod";
 import { mastra } from "./index";
 import { cheatcodeTools } from "./tool-defs/tool-set";
 
+export const GeneralAgentFinishReasonSchema = z.enum([
+  "stop",
+  "length",
+  "content-filter",
+  "tool-calls",
+  "error",
+  "other",
+]);
+
+type GeneralAgentFinishReason = z.infer<typeof GeneralAgentFinishReasonSchema>;
+
 export interface GeneralAgentToolCall {
   input: JSONValue;
   toolCallId: string;
   toolName: string;
 }
 
 interface GeneralAgentStepResult {
-  finishReason: string;
+  finishReason: GeneralAgentFinishReason;
   responseMessages: JSONValue[];
   text: string;
   toolCalls: GeneralAgentToolCall[];
@@ -51,7 +62,7 @@ export async function generateGeneralAgentStep(
     runId: options.runId,
   });
   return {
-    finishReason: result.finishReason ?? "unknown",
+    finishReason: GeneralAgentFinishReasonSchema.parse(result.finishReason),
     responseMessages: toJsonValues(result.response.messages ?? []),
     text: result.text,
     toolCalls: result.toolCalls.map((call) => ({