clawdbot-workspace/research-agent-orchestration-patterns.md

Agent Orchestration Patterns & Interactive Workflow Frameworks

Deep Research Report — February 2026

Purpose: Comprehensive comparison of how modern AI agent frameworks handle multi-agent orchestration with human-in-the-loop (HITL), state persistence, interactive UIs, and notification patterns — with architectural recommendations for building an interactive AI factory.

---

Table of Contents

1. Agent Orchestration Frameworks — Deep Comparison
2. State Machine Patterns for Agent Pipelines
3. Notification & Alerting Patterns
4. Chat-Embedded Interactive Modules
5. MCP Apps / Interactive MCP Patterns
6. Production Examples
7. Architectural Recommendations

---

1. Agent Orchestration Frameworks

LangGraph — The Gold Standard for HITL

LangGraph is currently the most mature framework for human-in-the-loop agent workflows. It provides three core primitives:

Three Pillars: Checkpointing → Interrupts → Commands

1. Checkpointing — Persistent state that survives crashes, restarts, and even server migrations. Analogous to BizTalk's dehydration/rehydration pattern. The full agent state (variables, context, progress) is serialized to a backend (MemorySaver for dev, PostgresSaver/SQLiteSaver for production).

2. Interrupts — Two flavors:

   • Static: Always pause at a specific node (interrupt_before=["sensitive_action"])
   • Dynamic: Conditionally pause based on runtime state using interrupt() from langgraph.types

3. Commands — Resume a paused workflow with Command(resume={...}), correlated by thread_id

# LangGraph HITL pattern — dynamic interrupt
from langgraph.types import interrupt, Command
from langgraph.checkpoint.memory import MemorySaver

def process_transaction(state):
    if state["transaction_amount"] > 10000:
        human_decision = interrupt({
            "question": f"Approve transaction of ${state['transaction_amount']}?",
            "details": state["details"]
        })
        if not human_decision.get("approved"):
            return {"status": "rejected", "reason": human_decision.get("reason")}
    return {"status": "approved", "processed": True}

graph = workflow.compile(checkpointer=MemorySaver())
config = {"configurable": {"thread_id": "txn-123"}}
result = graph.invoke(initial_state, config)

# Later (hours/days), resume with human input:
result = graph.invoke(
    Command(resume={"approved": True, "notes": "Verified identity"}),
    config
)

Key Insight: The graph doesn't replay from the start — it resumes from the exact checkpoint. The thread_id acts as a correlation key (similar to BizTalk correlation sets).

Production template: There's an open-source LangGraph interrupt workflow template with FastAPI + Next.js frontend that demonstrates the full pattern.

---

CrewAI — Simpler but Less Flexible

CrewAI supports HITL through two mechanisms:

1. human_input=True on Tasks — When a task has this flag, the agent pauses after completing its work and asks the human to review/approve before finalizing. This is a task-level checkpoint, not node-level.

2. allow_delegation=True on Agents — Enables agents to delegate work to other agents, with optional human input at delegation points.

3. Hierarchical Process — Automatically assigns a manager agent that coordinates planning, delegation, and validation. The manager can route to humans.

# CrewAI HITL pattern
task1 = Task(
    description="Conduct analysis of AI trends in 2024...",
    expected_output="Detailed report",
    human_input=True,  # Pauses for human review
    agent=researcher_agent
)

Limitations:

• No persistent checkpoint/resume like LangGraph — if the process crashes while waiting for human input, state is lost
• human_input is essentially a blocking input() call under the hood
• No dynamic interrupt capability (always pauses or never pauses, per task config)
• CrewAI Flows (newer) add start/listen/router steps with state persistence and resume, but HITL is still less mature than LangGraph

---

AutoGen — Conversation-Centric HITL

AutoGen (now at v0.4+ / "stable") models HITL through the UserProxyAgent:

1. UserProxyAgent — A special agent that acts as a proxy for human input. It blocks the team's execution until the user responds.

2. Group Chat Orchestration — In RoundRobinGroupChat, the UserProxyAgent is called in order. In SelectorGroupChat, a selector prompt/function dynamically decides when to route to the human.

3. human_input_mode (v0.2) — Three modes: ALWAYS (always ask), TERMINATE (ask only at termination), NEVER (fully autonomous).

# AutoGen HITL pattern
from autogen_agentchat.agents import AssistantAgent, UserProxyAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import TextMentionTermination

user_proxy = UserProxyAgent("user_proxy", input_func=input)
assistant = AssistantAgent("assistant", model_client=model_client)
termination = TextMentionTermination("APPROVE")

team = RoundRobinGroupChat([assistant, user_proxy], termination_condition=termination)
await team.run_stream(task="Write a 4-line poem about the ocean.")

Key Limitation: UserProxyAgent blocks the entire team execution. AutoGen docs explicitly recommend it only for "short interactions requiring immediate feedback" like button clicks. It puts the team in an unstable state that cannot be saved or resumed. For long-running approvals, you need external patterns.

Integration examples: AutoGen provides sample integrations with FastAPI, ChainLit, and Streamlit for web-based HITL.

---

Microsoft Semantic Kernel — Process Framework with External Pub/Sub

Semantic Kernel's Process Framework (experimental, as of Feb 2026) takes an event-driven approach to HITL:

1. Parameter Gating — A step's KernelFunction only executes when ALL required parameters are provided. By adding a userApproval parameter, the step naturally waits for both the document AND the approval.

2. ProxyStep + External Pub/Sub — A ProxyStep bridges internal process events to external messaging systems. When a document is approved by the AI proofreader, an event is emitted externally via IExternalKernelProcessMessageChannel.

3. External Event Injection — Human decisions come back as OnInputEvent("UserApprovedDocument") that route to the waiting step's parameter.

// Semantic Kernel Process: parameter gating for HITL
public class PublishDocumentationStep : KernelProcessStep
{
    [KernelFunction]
    public DocumentInfo PublishDocumentation(
        DocumentInfo document,     // From AI proofreader
        bool userApproval          // From human via external pub/sub
    )
    {
        if (userApproval) { /* publish */ }
        return document;
    }
}

// Process wiring
processBuilder
    .OnInputEvent("UserApprovedDocument")
    .SendEventTo(new(docsPublishStep, parameterName: "userApproval"));

Architectural Insight: This is the most "enterprise-ready" pattern — clean separation between the process engine and the notification/approval system. The IExternalKernelProcessMessageChannel interface can be implemented for any pub/sub backend (Azure Service Bus, Redis, Kafka, etc.).

---

Temporal.io — The Durability Champion

Temporal provides the most robust HITL primitive through Signals:

1. Signals — Asynchronous messages sent to a running Workflow to change its state. A @workflow.signal handler mutates workflow state, and the main workflow loop uses workflow.wait_condition() to react.

2. Queries — Read-only inspection of workflow state (e.g., "what's the current LLM output?") without affecting execution.

3. Updates — Synchronous tracked write requests where the sender can wait for a response.

# Temporal HITL pattern
class UserDecision(StrEnum):
    KEEP = "KEEP"
    EDIT = "EDIT"
    WAIT = "WAIT"

@workflow.defn
class ResearchWorkflow:
    def __init__(self):
        self._user_decision = UserDecisionSignal(decision=UserDecision.WAIT)

    @workflow.signal
    def user_decision(self, input: UserDecisionSignal):
        self._user_decision = input

    @workflow.run
    async def run(self, input):
        continue_loop = True
        while continue_loop:
            research = await workflow.execute_activity(llm_call, ...)
            # Wait for human signal
            await workflow.wait_condition(
                lambda: self._user_decision.decision != UserDecision.WAIT
            )
            if self._user_decision.decision == UserDecision.KEEP:
                continue_loop = False
            elif self._user_decision.decision == UserDecision.EDIT:
                # Incorporate feedback, reset, loop
                self._user_decision.decision = UserDecision.WAIT

Why Temporal is unmatched for durability:

• Workflow state survives crashes, restarts, and server migrations automatically
• No separate checkpointing step needed — it's intrinsic to the execution model
• Signals are durably stored — if a user approves and the server crashes, the approval is NOT lost
• Workflows can run for months/years without consuming resources while waiting
• Built-in retry, timeout, and heartbeat mechanisms

---

Inngest — Serverless Step Functions with Event Matching

Inngest's step.waitForEvent() is elegant for serverless HITL:

// Inngest HITL pattern
const processInvoice = inngest.createFunction(
  { id: "process-invoice" },
  { event: "app/invoice.created" },
  async ({ event, step }) => {
    const analysis = await step.run("analyze", () => analyzeInvoice(event.data));

    // Wait up to 7 days for human approval, match by invoiceId
    const approval = await step.waitForEvent("wait-for-approval", {
      event: "app/invoice.approved",
      timeout: "7d",
      match: "data.invoiceId",  // Correlation!
    });

    if (!approval) {
      await step.run("escalate", () => notifyManager(event.data));
      return;
    }

    await step.run("process", () => processPayment(approval.data));
  }
);

Key Features:

• Event correlation via match — automatically matches approval events to the correct waiting function by field value
• Timeouts with fallback logic — if no approval in 7 days, escalate
• Serverless — no persistent server needed; the function is dehydrated and rehydrated on events
• Realtime streaming — Combine with Inngest Realtime to stream status updates to the UI while waiting

Limitation: The waitForEvent only listens for events from when the step executes — events sent before the wait starts are missed (lookback feature planned).

---

Prefect / Dagster — Data Pipeline Focus, Limited HITL

Neither Prefect nor Dagster has first-class HITL primitives. They're optimized for data pipeline orchestration, not human-interactive workflows.

• Dagster: Sensors can trigger on external events, but there's no built-in "wait for human approval" gate. You'd need to implement it externally (e.g., poll a database for approval status).
• Prefect: Similar story — you can use pause/resume via the API, but it's not a core workflow primitive. Tasks are primarily designed for data transformations.
• Both: The Reddit consensus is "if you need human-interactive workflows, use Temporal.io instead."

---

2. State Machine Patterns for Agent Pipelines

XState + Stately Agent — State Machines for LLM Agents

Stately Agent (formerly @statelyai/agent) combines XState v5 state machines with LLM decision-making:

• State machines guide agent behavior — Valid transitions are defined explicitly, preventing the agent from entering invalid states
• Observations, feedback, and insights feed into decision-making
• First-class Vercel AI SDK integration — supports OpenAI, Anthropic, Google, Mistral, Groq, etc.
• Episodes — complete sequences from initial state to goal (similar to RL episodes)

Modeling "Waiting for Human" as a State:

// XState pattern: waiting-for-human as first-class state
const agentMachine = createMachine({
  id: 'pipeline',
  initial: 'gathering',
  states: {
    gathering: {
      invoke: { src: 'gatherData', onDone: 'analyzing' }
    },
    analyzing: {
      invoke: { src: 'runAnalysis', onDone: 'awaitingHumanReview' }
    },
    awaitingHumanReview: {
      // This is a "parking" state — no automatic transitions
      // Only human events can move forward
      on: {
        APPROVE: { target: 'publishing', actions: 'recordApproval' },
        REJECT: { target: 'revising', actions: 'recordRejection' },
        EDIT: { target: 'analyzing', actions: 'incorporateFeedback' }
      },
      after: {
        // Auto-escalate after 24 hours
        86400000: { target: 'escalating' }
      }
    },
    escalating: {
      invoke: { src: 'notifyManager', onDone: 'awaitingHumanReview' }
    },
    revising: {
      invoke: { src: 'applyRevisions', onDone: 'awaitingHumanReview' }
    },
    publishing: {
      invoke: { src: 'publishResult', onDone: 'complete' }
    },
    complete: { type: 'final' }
  }
});

Persistent State Machines with Restate: Restate.dev offers persistent serverless state machines that combine XState's modeling with durable execution — state machines survive crashes and can be distributed across serverless functions.

Key Patterns:

• Parallel states for concurrent agent work with sync points (XState parallel states)
• Guard conditions for branching based on human decisions
• Delayed transitions (after) for SLA enforcement and auto-escalation
• Snapshot/restore — XState v5 supports persisting machine state to any backend

---

3. Notification & Alerting Patterns

Making Human Input OBVIOUS

The n8n community and PagerDuty have established robust patterns:

Escalation Chain Pattern (PagerDuty-style):

Level 1 (0 min):  Slack notification + email
Level 2 (15 min): Direct message + phone push notification  
Level 3 (30 min): SMS to secondary reviewer
Level 4 (60 min): Phone call to manager
Level 5 (4 hrs):  Auto-default to safest outcome + incident report

SLA Tracking:

• Record waiting_since timestamp when entering HITL state
• Display elapsed time in all notifications ("⏰ Waiting 2h 15m")
• Color-code: 🟢 < 1hr, 🟡 1-4hr, 🔴 > 4hr, 🚨 > SLA threshold
• Dashboard showing all pending approvals with age

Smart Batching (Context-Rich Notifications): Instead of 10 separate "approve this" notifications:

📋 5 MCP server builds need review:
┌────────────────────────────────────┐
│ 1. ✅ ghl-mcp (tests pass, low risk)    [Approve] [Review]
│ 2. ⚠️ stripe-mcp (1 warning, med risk)  [Approve] [Review]
│ 3. ❌ shopify-mcp (2 errors, high risk)  [Review Required]
│ 4. ✅ notion-mcp (tests pass, low risk)  [Approve] [Review]
│ 5. ✅ cal-mcp (tests pass, low risk)     [Approve] [Review]
└────────────────────────────────────┘
⏰ Oldest: 45 min ago | 🎯 SLA: 2 hours
[Approve All Low-Risk] [Review All]

n8n Escalation Pattern:

Wait Node (timeout: 2h)
  → IF approved → continue
  → IF timed out → notify backup owner
    → Wait Node (timeout: 1h)
      → IF approved → continue  
      → IF timed out → auto-reject + incident log

---

4. Chat-Embedded Interactive Modules

Slack Block Kit — The Reference Implementation

Slack Block Kit provides the canonical pattern for interactive chat elements:

{
  "blocks": [
    {
      "type": "header",
      "text": { "type": "plain_text", "text": "🏭 MCP Build Review Required" }
    },
    {
      "type": "section",
      "fields": [
        { "type": "mrkdwn", "text": "*Server:*\nghl-mcp" },
        { "type": "mrkdwn", "text": "*Status:*\n✅ Tests Passing" }
      ]
    },
    {
      "type": "section",
      "fields": [
        { "type": "mrkdwn", "text": "*Build Time:*\n2m 34s" },
        { "type": "mrkdwn", "text": "*Risk Level:*\n🟢 Low" }
      ]
    },
    {
      "type": "actions",
      "elements": [
        { "type": "button", "text": { "type": "plain_text", "text": "✅ Approve" }, "style": "primary", "value": "approve" },
        { "type": "button", "text": { "type": "plain_text", "text": "❌ Reject" }, "style": "danger", "value": "reject" },
        { "type": "button", "text": { "type": "plain_text", "text": "👀 Review Details" }, "value": "review" }
      ]
    }
  ]
}

Discord Components — Buttons + Embeds

Discord supports interactive components via discord.js:

const row = new ActionRowBuilder().addComponents(
  new ButtonBuilder()
    .setCustomId('approve_build_123')
    .setLabel('✅ Approve')
    .setStyle(ButtonStyle.Success),
  new ButtonBuilder()
    .setCustomId('reject_build_123')
    .setLabel('❌ Reject')
    .setStyle(ButtonStyle.Danger),
  new ButtonBuilder()
    .setCustomId('details_build_123')
    .setLabel('📋 Details')
    .setStyle(ButtonStyle.Secondary)
);

const embed = new EmbedBuilder()
  .setTitle('🏭 Build Review: ghl-mcp')
  .addFields(
    { name: 'Status', value: '✅ Tests Passing', inline: true },
    { name: 'Risk', value: '🟢 Low', inline: true },
    { name: 'Waiting', value: '⏰ 15 minutes', inline: true }
  )
  .setColor(0x00ff00);

await channel.send({ embeds: [embed], components: [row] });

Interactive Patterns for Chat:

• Card-based reviews — Embed with context + action buttons
• Select menus — Choose from options (e.g., select which variant to deploy)
• Modal forms — Full form inputs triggered by button click (Discord modals, Slack dialogs)
• Progress indicators — Update embed fields in-place as pipeline progresses
• Threaded detail — "Review Details" button creates a thread with full diff/logs

---

5. MCP Apps / Interactive MCP Patterns

MCP Apps Extension — Interactive UIs in Chat (January 2026)

The MCP Apps specification (released Nov 2025, refined Jan 2026) is a game-changer for HITL:

Core Pattern:

1. A tool declares _meta.ui.resourceUri pointing to a ui:// resource
2. The host fetches the HTML resource and renders it in a sandboxed iframe
3. Bidirectional communication via JSON-RPC between the app and host

// MCP Apps: Interactive approval UI served by MCP server
import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
import { registerAppTool, registerAppResource } from '@modelcontextprotocol/ext-apps/server';
import { createUIResource } from '@mcp-ui/server';

const approvalUI = createUIResource({
  uri: 'ui://factory/build-approval',
  content: {
    type: 'rawHtml',
    htmlString: `
      <div id="approval">
        <h2>Build Review</h2>
        <div id="details">Loading...</div>
        <button onclick="approve()">✅ Approve</button>
        <button onclick="reject()">❌ Reject</button>
      </div>
      <script>
        // Receives tool input/result from host
        window.addEventListener('message', (e) => {
          document.getElementById('details').innerHTML = e.data.buildSummary;
        });
        async function approve() {
          // Call back to MCP server tool
          window.parent.postMessage({ method: 'tools/call', params: {
            name: 'approve_build', arguments: { buildId: '123', approved: true }
          }}, '*');
        }
      </script>
    `
  },
  encoding: 'text'
});

registerAppResource(server, 'approval_ui', approvalUI.resource.uri, {}, async () => ({
  contents: [approvalUI.resource]
}));

registerAppTool(server, 'review_build', {
  description: 'Show build review interface',
  inputSchema: { buildId: z.string() },
  _meta: { ui: { resourceUri: approvalUI.resource.uri } }
}, async ({ buildId }) => {
  const build = await getBuildDetails(buildId);
  return { content: [{ type: 'text', text: JSON.stringify(build) }] };
});

Why MCP Apps Matter for AI Factories:

• Context preservation — The approval UI lives inside the conversation, no tab switching
• Bidirectional data flow — The app can call MCP tools, and the host can push data to the app
• Security — Sandboxed iframe, can't escape container
• Use cases: Forms, dashboards, real-time monitoring, multi-step approval workflows, code review with diff viewer

MCP Resources for Dashboard Data

MCP resources can expose real-time pipeline state:

// Expose factory dashboard data as MCP resource
server.resource("factory-status", "factory://status/current", async () => ({
  contents: [{
    uri: "factory://status/current",
    mimeType: "application/json",
    text: JSON.stringify({
      pendingApprovals: 3,
      activePipelines: 7,
      completedToday: 23,
      oldestWaiting: "45 minutes"
    })
  }]
}));

MCP Sampling for Agent-to-Agent Communication

MCP sampling allows a server to request LLM completions through the client, enabling:

• Agent orchestrator requests analysis from a specialized agent via sampling
• Multi-step reasoning where one MCP server delegates complex decisions to the LLM
• Pattern: Factory coordinator agent uses sampling to ask specialized agents for build quality assessments

---

6. Production Examples

Retool Workflows — Approval Steps

Retool Workflows offer native approval steps that "wait intelligently for human input." Multi-step processes span software, data, and people. Approval steps pause the workflow, notify the approver, and resume on response. Clean UI for non-technical reviewers.

Slack Workflow Builder — Human Steps

Slack's native workflow builder allows inserting "Send a form" steps that collect human input mid-workflow, with the response routed to subsequent steps. Combined with Block Kit interactive messages for richer approval UIs.

PagerDuty Escalation Policies

Production-proven escalation pattern:

• Level 1: Primary on-call (immediate notification via configured channels)
• Level 2: Secondary on-call (escalates after X minutes of non-acknowledgment)
• Level 3: Management (escalates if Level 2 doesn't respond)
• On-call handoff notifications — notify when responsibility changes
• SLA-aware — escalation timing tied to service tier and SLOs

GitOps Approval Patterns (ArgoCD / Flux)

• PR-based approvals: Changes to deployment manifests require PR review → merge → deploy
• Environment promotion: dev → staging → prod with manual sync gates in ArgoCD
• ArgoCD Sync Policies: Can require manual sync (human clicks "Sync" in UI) or auto-sync per environment
• Flux Gate objects (proposed): Dedicated API for defining Gate objects with open/close operations for manual gating
• Pattern: PR to main → auto-deploy to dev → await approval → deploy to staging → await → deploy to prod

n8n HITL Patterns (Production)

The n8n community has developed robust patterns:

• Wait Node + Webhook: Workflow pauses at Wait node, sends notification with unique approval URL, resumes when URL is hit
• Timeout + Escalation: Wait node with timeout → IF branch → escalate or auto-default
• Supabase trigger pattern: Write task to DB, pause at Wait, Supabase trigger fires on row update to resume
• Audit logging: Every HITL decision recorded with timestamp, user, decision, and context

---

7. Architectural Recommendations

For Building an Interactive AI Factory

Based on this research, here's the recommended architecture stack:

Core Orchestration: Hybrid LangGraph + Temporal

• LangGraph for AI agent pipelines (LLM calls, tool use, reasoning chains) — its interrupt/Command pattern is purpose-built for AI workflows
• Temporal for the outer durable workflow envelope — wraps LangGraph agent runs in a crash-proof, signal-capable workflow that can wait indefinitely for human input
• This gives you LangGraph's AI-native patterns PLUS Temporal's enterprise-grade durability

State Management: XState-Inspired State Machines

• Model pipeline states explicitly: gathering → analyzing → awaitingReview → publishing → complete
• "Waiting for human" is a first-class state with defined transitions (approve/reject/edit)
• after transitions for auto-escalation and SLA enforcement
• Persist state snapshots for checkpoint/resume across process boundaries

Notification Layer: PagerDuty-Style Escalation

Event → Discord Embed (immediate)
  → [15 min] Discord DM + Push notification
  → [30 min] SMS/phone
  → [SLA breach] Auto-default + incident log

Interactive UI: Discord Components + MCP Apps

• Discord buttons/embeds for quick approve/reject decisions (low friction)
• MCP Apps for complex reviews (build diffs, multi-field forms, dashboards)
• Smart batching — group similar approvals into one interactive message

Event Backbone: Inngest-Style Event Matching

• step.waitForEvent() pattern with correlation by entity ID
• Timeout-based fallbacks built into every human wait
• Event-driven architecture keeps everything decoupled

Key Design Principles

1. Every human wait MUST have a timeout — No infinite hangs. Default to safest outcome + escalation.
2. Context-rich notifications — Include enough info to decide without opening another tool.
3. Correlation keys everywhere — Every approval request carries a unique ID that traces back to the pipeline run.
4. Idempotent approvals — Clicking "Approve" twice shouldn't double-process.
5. Audit trail — Every human decision is logged with who, when, what, and why.
6. Graceful degradation — If the human layer is unreachable, the system should degrade gracefully (queue, retry, escalate) not crash.

Framework Selection Matrix

Framework	HITL Maturity	Durability	Ease of Use	Best For
LangGraph	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	AI agent pipelines with human checkpoints
Temporal	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	Long-running workflows, mission-critical
Inngest	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Serverless, event-driven, TypeScript-first
AutoGen	⭐⭐	⭐	⭐⭐⭐⭐	Conversational multi-agent, quick prototypes
CrewAI	⭐⭐	⭐⭐	⭐⭐⭐⭐⭐	Simple agent crews, rapid development
Semantic Kernel	⭐⭐⭐	⭐⭐⭐	⭐⭐	.NET/Enterprise, Azure-heavy stacks
MCP Apps	⭐⭐⭐⭐	N/A	⭐⭐⭐	Rich interactive UIs within AI chat
n8n	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	Visual workflow builder, non-code HITL

Recommended Stack for Clawdbot AI Factory

┌─────────────────────────────────────────────────┐
│           Discord Interface Layer                │
│  Embeds + Buttons + Modals + Thread Details      │
├─────────────────────────────────────────────────┤
│          MCP Apps (Complex Reviews)              │
│  Build Diffs · Forms · Dashboards · Code Review  │
├─────────────────────────────────────────────────┤
│         Agent Orchestration (LangGraph)          │
│  interrupt() · Command(resume) · Checkpointing   │
├─────────────────────────────────────────────────┤
│         Durable Execution (Temporal/Inngest)     │
│  Signals · waitForEvent · Crash Recovery          │
├─────────────────────────────────────────────────┤
│         State Machine (XState-inspired)          │
│  Pipeline States · SLA Timers · Escalation        │
├─────────────────────────────────────────────────┤
│         Notification Engine                       │
│  Escalation Chains · Batching · SLA Tracking      │
├─────────────────────────────────────────────────┤
│         Persistence Layer                         │
│  PostgreSQL · Redis · Event Store                 │
└─────────────────────────────────────────────────┘

---

Report compiled February 2026. Sources: LangGraph docs, CrewAI docs, AutoGen stable docs, Temporal tutorials, Inngest docs, MCP Apps specification, Semantic Kernel Process Framework docs, PagerDuty escalation docs, Slack Block Kit docs, n8n community patterns, and community discussions.