Name: Orchestrate
Author: Orchestrate

Pure LLM Agents Aren't Enough

LLMs are powerful for reasoning and language understanding—but they have well-known limitations:

Inconsistent outputs that vary between runs
No deterministic state management
Difficult to audit for compliance
Can't enforce business rules reliably
No native integration with ML models

Traditional workflow engines handle process flow—but can't adapt to ambiguity. Pure agent frameworks provide flexibility—but sacrifice transparency and control.

The Orchestrate Architecture

Five Layers. Right Tool for Right Task.

Layer	Technology	Responsibility
Interaction	LLM Agents	Natural language understanding, intent extraction, response generation
Orchestration	Workflow Engine	State management, process flow, agent coordination, pause/resume
Decision	Business Rules	Deterministic decisions, eligibility checks, policy enforcement
Intelligence	ML/AI Platform	Risk scoring, fraud detection, credit modeling, prediction
Data	Data Platform	Customer data, credit bureau, feature store, documents

Orchestrated Intelligence: Business rules provide the guardrails. AI agents handle the judgment calls. Every decision is auditable. Every method serves its purpose.

Built on Five Specialized Layers

Interaction layer for natural language. Orchestration layer for state and flow. Decision layer for business rules. Intelligence layer for ML scoring. Data layer for integration. Each layer uses the right technology for its specific job.

Enterprise-Ready from Day One

Circuit breakers prevent cascading failures. Retry policies with exponential backoff handle transient errors. Event-driven architecture eliminates polling. Full observability shows you exactly what's happening at every layer.

Build Complex Workflows from Simple Components

Package workflows as self-contained units with input/output contracts. Compose using call steps, fork-join parallelism, or inline expansion. Publish capability cards for AI-powered discovery.

1

Natural Language Intent

Chat Agent extracts structured intent

2

Intelligent Routing

Semantic search across capability cards

3

Coordinated Execution

Stateful workflow orchestration

4

Multi-Method Intelligence

Rules + ML + LLM working together

Step 1: Chat Agent (Interaction Layer)

Customer describes what they want. Chat Agent extracts structured intent using Claude Sonnet with temperature 0.3 for consistency. Output includes action type, entities, and confidence scores.

{ "intent": "loan_application", "entity": "boat", "amount": 45000, "confidence": 0.92 }

Step 2: Orchestration Agent (Orchestration Layer)

Performs semantic search across workflow capability cards. Matches intent to registered workflows using vector similarity. Filters by user permissions and extracts required parameters.

Routes to loan_application_workflow with customer context

Step 3: Workflow Agent (Orchestration Layer)

Stateful workflow orchestrates the entire process. Event-driven execution pauses for external events—API responses, human approvals, time delays. Fork-join processes data sources in parallel.

Step 4: Multiple Agent Types Working Together

Policy Agent answers questions via RAG. Decision Agent runs deterministic checks. ML Scoring Agent predicts risk. Ingestion Agent processes documents. Each uses the right method for its task.

1

Import

Docs & APIs

2

Extract

5 Layers

3

Review

Human-in-the-Loop

4

Test

Mock Infrastructure

5

Deploy

Package & MCP

6

Monitor

5-Layer Trace

Step 1

Start With What Already Exists

Orchestrate ingests existing documentation and automatically identifies which tasks require which methods. Upload SOPs and process docs—we identify state management needs, eligibility logic, document understanding, and prediction needs.

Process Documentation

SOPs, runbooks, process manuals (PDF, Word, Confluence)

Business logic descriptions and decision criteria

Exception handling and escalation procedures

API & Tool Integrations

OpenAPI specifications (REST APIs)

MCP server manifests (Model Context Protocol)

A2A cards (Agent-to-Agent communication)

GraphQL schemas, existing scripts

Agent Capability Cards

Pre-built agent metadata for discovery

Intent patterns and routing rules

Security policies and access control

Import

Drop files here or click to browse

SOPs, API specs, MCP manifests, capability cards

PDF DOCX OpenAPI YAML GraphQL MCP

Multi-Method Recognition

LLM Agents Intent & Explanation

Workflow State Management

Business Rules Eligibility Logic

ML Models Risk Scoring

Data Integration Credit Bureau, CRM

Step 2

LLM-Powered Multi-Method Generation

Our extraction engine reads your documentation and generates a complete multi-layer workflow—identifying which tasks belong in which architectural layer.

What The Extraction Finds

Layer	Identifies	Examples
Interaction	NLU, intent extraction, response generation, RAG	Customer Q&A, document-grounded answers
Orchestration	Sequential/parallel steps, state, pause/resume	Multi-step application, fork-join enrichment
Decision	Eligibility, policy enforcement, audit trails	Qualification logic, compliance rules
Intelligence	Risk scoring, fraud detection, predictions	Credit risk models, propensity scoring
Data	API dependencies, DB queries, feature stores	Credit bureau calls, document storage

workflow_package:
  name: "boat_loan_application"
  version: "1.0.0"

  agents:
    - type: chat_agent
      layer: interaction
      llm: claude-sonnet-4
      purpose: "Customer intent extraction"

    - type: workflow_agent
      layer: orchestration
      engine: orchestrate
      purpose: "Multi-step application process"

    - type: decision_agent
      layer: decision
      implementation: business_rules
      purpose: "Eligibility checks"

    - type: scoring_agent
      layer: intelligence
      implementation: ml_platform
      purpose: "Credit risk and fraud scoring"

  composition:
    - pattern: fork_join
      parallel_branches:
        - customer_data_agent
        - credit_bureau_agent
        - fraud_scoring_agent
      completion_policy: wait_for_all

Step 3

You Control the Architecture

LLM extraction proposes the multi-method architecture—you decide the final implementation. Review agent composition across all five layers. Swap LLM-based agents for business rules where consistency matters. Nothing deploys without your approval.

Review Capabilities

Verify tasks are assigned to appropriate architectural layers
Configure LLM temperature, prompts, output schemas
Choose between call step, fork-join, or inline expansion
Refine intent patterns for semantic routing
Set retry policies, timeouts, circuit breaker thresholds

Review — Layer View

Adjustment Examples

Original Risk assessment in LLM Agent

Changed Move to ML Scoring Agent (Intelligence Layer)

Predictions should use trained models, not LLM reasoning

Original Eligibility via LLM reasoning

Changed Business Rules Decision Agent

Eligibility must be deterministic for compliance

Added Explainer Agent (Interaction Layer)

Regulatory requirement for fair lending explanations

Approve & Continue Edit Layer

Step 4

Validate All Five Layers Before Production

Test your multi-method workflow in our playground with complete mock infrastructure. Simulate external APIs, databases, ML models, and services. See distributed traces across all five layers—before any real data flows through.

Mock Infrastructure by Layer

Interaction Mock LLM & RAG responses

Orchestration Simulate pause/resume events

Decision Mock rule evaluations

Intelligence Mock ML predictions & scores

Data Mock APIs, DB, credit bureau

Test Playground

Happy Path: “Perfect boat loan application”

Duration: 1.8s · 9 agents · 5 layers

✓ Interaction Intent extracted (confidence: 0.94) 85ms

✓ Orchestration Workflow initiated, state persisted 30ms

✓ Decision Eligibility checks passed 95ms

✓ Intelligence Risk score 65 (below threshold) 115ms

✓ Data All external calls succeeded 245ms

✓ Decision Final decision: APPROVE 70ms

Error Cascade: “Credit bureau API failure”

✓ Interaction Intent extracted correctly 85ms

× Data Credit bureau API timeout 3000ms

→ Orchestration Retry policy triggered (attempt 2) 200ms

→ Orchestration Circuit breaker opened — workflow paused —

✓ Interaction Customer notified of delay 60ms

Result: Graceful degradation, no cascading failures

Step 5

One-Click Multi-Layer Deployment

When testing passes, deploy with confidence. Your workflow is packaged as a self-contained unit with all agents, contracts, capability cards, and metadata. Deployed via MCP for automatic discovery. Staged rollouts. Automatic rollback.

Deployment Options

Immediate

Full deployment now. All layers updated simultaneously.

Staged

10% → 50% → 100%. Progressive rollout across layers.

Canary

Side-by-side with old version. Both active, comparing outcomes.

Scheduled

Deploy at specific time. Minimizes disruption during business hours.

boat_loan_application_v1.0.0.pkg
├── manifest.yaml          # Metadata, version, deps
├── webhooks/
│   ├── ingress.yaml        # HTTP, event, MCP entry
│   └── egress.yaml         # Integration points
├── contract/
│   ├── input_schema.json  # Expected inputs
│   ├── output_schema.json # Guaranteed outputs
│   └── context_docs.md    # Context patterns
├── workflow/
│   ├── orchestration.yaml # Step definitions
│   └── codelets/
│       ├── chat_agent.py
│       ├── eligibility.rules
│       └── scoring.yaml
├── ai_metadata/
│   ├── capability_card.yaml # Semantic discovery
│   ├── intent_patterns.json # Routing
│   └── security_policy.yaml # RBAC/ABAC
└── tests/
    ├── happy_path.yaml
    └── error_scenarios.yaml

mcp_server:
  name: "boat_loan_application"
  version: "1.0.0"

  capabilities:
    - description: "Process boat loan applications"
      verbs: ["apply", "request", "check eligibility"]
      entities: ["boat loan", "marine vessel"]

  security:
    roles_required: ["customer", "call_center_agent"]
    data_classification: "PII_FINANCIAL"

Step 6

Comprehensive Multi-Layer Observability

Most observability tools hand you logs and traces — then leave you to figure out the rest. Orchestrate's built-in Debugging Agent goes further: it analyzes anomalies, pinpoints root causes, and surfaces actionable fixes automatically.

No more correlating traces across dashboards. No more escalating tickets to domain experts. Orchestrate closes the loop between detection and resolution — so your team spends less time firefighting and more time building.

Monitoring Capabilities

Built-in Debugging Agent

Analyzes anomalies, pinpoints root causes, and surfaces actionable fixes — automatically, without human intervention.

Automated Root Cause Analysis

No more correlating traces across dashboards. Orchestrate identifies the root cause and recommends corrective actions.

Closed-Loop Resolution

Closes the loop between detection and resolution. No more escalating tickets to domain experts.

Less Firefighting, More Building

Your team spends less time troubleshooting and more time building — Orchestrate handles the operational burden.

Observability Dashboard

boat_loan_application Operational

Uptime

99.97%

Today

12.4K

Requests

Active

347

Workflows

Interaction — Chat Agent 820ms · 99.1%

Orchestration — 347 active 87% completion

Decision — Eligibility 78% approved

Intelligence — Credit Risk 94% accuracy

Data — Credit Bureau 312ms · 97.8%

⚠ Credit Bureau circuit breaker triggered (12:34 PM) — 23 workflows paused

Orchestrated Intelligence

Why Architecture Matters