Skip to content

3. Advanced Features: Orchestration of Complex Agent Networks

If defining roles with the agent keyword is just creating individual gears, then the true "industrial revolution" in the Nexa universe is embodied in its unparalleled collaborative orchestration network.

Nexa's core design moat since the v0.5 architecture upgrade has been elevating complex, asynchronous and race-condition-filled multi-stage agent interactions to the level of language keywords. In this chapter, we'll explore in detail how to leverage pipelines (>>), intent routing (match intent), concurrent aggregation (join), DAG operators, and the incredible semantic loops (loop until) in enterprise-grade Agent frameworks.

🛤️ Pipeline Operator: >> (Pipeline Data Bus)

In most tasks (such as academic translation, code writing and testing chains), multi-agent collaboration is simply a pure pipeline: the output of the previous Agent (Output Task) along with its historical context should be passed completely and without loss as input to the next Agent.

Pain Points of Traditional Approach

If we use the traditional approach represented by LangChain to build this chain segment:

# Pain points of traditional languages: filled with intermediate variables and implicit state loss
draft = Writer.run(topic)
translated_draft = Translator.run(draft)
reviewer_feedback = Reviewer.run(translated_draft)

You'll find that not only do you define a bunch of single-use intermediate variables (leading to poor memory management), but you're also prone to various runtime errors due to different type wrappers (like BaseMessage objects vs plain str).

Nexa Pipeline Operator

In Nexa, we can use the expressive Unix-like pipeline operator >> in one go:

// From Nexa practical code collection (Pipeline Pattern)
flow main {
    res = Coder.run("Generate a fast sorting algorithm in Python") >> Reviewer >> HumanInterface;

    // The return result will directly be the final form produced by the pipeline end (HumanInterface)
}

Pipeline Operator Details

Operation Equivalent Code Description
A >> B B.run(A.run(input)) Pass A's output to B
A >> B >> C C.run(B.run(A.run(input))) Three-stage pipeline
input >> A >> B B.run(A.run(input)) Start from input

Compiler Magic Behind the Elegance

When you type A >> B, the Nexa compiler doesn't simply transpile it to linear function blocking calls. Under the hood, Nexa's Orchestrator automatically builds a DAG (Directed Acyclic Graph), which preserves the context sliding window from Coder to Reviewer, and strictly follows Promise ready state to activate the next node. This saves you countless lines of state maintenance code.

Pipeline Operator Complete Example

// Translation pipeline
agent Translator {
    role: "Professional Translator",
    prompt: "Translate English to Chinese, preserve original meaning, fluent language"
}

agent Proofreader {
    role: "Proofreading Editor",
    prompt: "Proofread translation, correct errors, polish language"
}

agent Formatter {
    role: "Formatting Expert",
    prompt: "Organize text into standard format"
}

flow main {
    english_text = "Artificial intelligence is transforming the world.";

    // Three-stage pipeline
    final_result = english_text >> Translator >> Proofreader >> Formatter;

    print(final_result);
}

Execution Result: 

人工智能正在改变世界。

🔀 Intent Routing: match intent Protocol

User instructions are always unpredictable. In many customer service/support bots, some users want to check the weather, some want to send emails, and others just want to hear a joke or chat.

If we use a "super large model" to handle all tasks:

  1. It consumes a large amount of billing tokens even when receiving trivial messages.
  2. Context is severely polluted, potentially incorrectly triggering system high-risk "delete database" tools when chatting.

Therefore, high-concurrency systems in the industry often advocate "front-end micro routing classifiers" (NLU/Intent Router) to achieve peak shaving and valley filling, then hand over actual processing to domain expert agents mounted behind.

Basic Syntax

match user_input {
    intent("intent description 1") => Agent1.run(user_input),
    intent("intent description 2") => Agent2.run(user_input),
    _ => DefaultAgent.run(user_input)  // Default branch
}

Complete Example

// Nexa in action: Multi-intent routing
flow main {
    req = "Tell me what is happening in the global tech world today!";

    // Using natural semantics and intent matching mechanism to directly decouple verbose and fragile if-else
    match req {
        intent("Check local weather") => WeatherBot.run(req) >> Translator,
        intent("Check daily news")    => NewsBot.run(req) >> Translator,
        _ => SmallTalkBot.run(req)
    }
}

Intent Routing Flow Diagram

User Input
    │
    ▼
┌─────────────────────┐
│  Intent Classifier  │
│    (Built-in)       │
│ Embedding + Similarity │
└─────────────────────┘
    │
    ├── intent("weather") ──────► WeatherBot
    │
    ├── intent("news") ──────► NewsBot
    │
    └── default (_) ────────────► SmallTalkBot

Parsing intent() Underlying Implementation

Here intent("...") is essentially not simple string or regex matching. Nexa internally carries an ultra-lightweight Embeddings matching model specifically for intent classification. In the background, it rapidly calculates cosine similarity with each branch's Condition, seamlessly routing execution flow into the most appropriate branch. To implement a similar mechanism in Python, you would need at least a ChromaDB service and a complex Top-K retrieval pool.

Intent Routing Best Practices

  1. Be specific with intent descriptions: Avoid vague descriptions
  2. Set reasonable default branch: Handle unknown intents
  3. Consider using fast models: Intent classification doesn't require complex reasoning
// ✅ Good intent descriptions
intent("Check weather forecast or current weather conditions")
intent("Check stock prices or financial data")
intent("Book flights, hotels, or restaurants")

// ❌ Bad intent descriptions
intent("weather")  // Too vague
intent("other")    // No practical meaning

🕸️ DAG Topology Orchestration: Ultimate Multi-way Fork and Merge (v0.9.7+)

When dealing with intelligence-intensive tasks (such as investment research report generation or core system difficult code optimization), it's necessary to have multiple roles conduct "back-to-back" independent research separately, then aggregate for cross-discussion. Nexa v0.9.7 revolutionarily introduced native handling of complex graph structures (DAG) topology operators.

DAG Operator Overview

Operator Name Description Example
>> Pipeline Sequential passing A >> B
|>> Fan-out Parallel send to multiple Agents input |>> [A, B, C]
&>> Merge/Fan-in Merge multiple results to one Agent [A, B] &>> C
?? Conditional Branch Select path based on condition input ?? A : B
|| Async Fork Send without waiting for result input || [A, B]
&& Consensus Merge Require all Agents to agree [A, B] && Judge

Fork Operator |>> (Fan-out)

Parallel clone upstream data and send to multiple Agents, waiting for all results to return.

// Fork operator - Parallel send to multiple Agents
flow main {
    topic = "Quantum Computing Applications";

    // Send topic to three Agents in parallel simultaneously
    results = topic |>> [Researcher, Analyst, Writer];

    // results is an array containing outputs from all three Agents
    print(results);
}

Flow Diagram: 

       topic
         │
         ├──────────┬──────────┐
         │          │          │
         ▼          ▼          ▼
    Researcher   Analyst    Writer
         │          │          │
         └──────────┴──────────┘
                    │
                    ▼
               [Result Array]

Use Cases: - Multi-angle analysis of the same problem - Multi-language translation - Multi-model comparison verification

Merge Operator &>> (Merge/Fan-in)

Merge outputs from multiple Agents and send to downstream Agent.

// Merge operator - Combine outputs from multiple Agents
flow main {
    // Wait for Researcher and Analyst to complete
    // Merge both outputs and send to Reviewer
    final_report = [Researcher, Analyst] &>> Reviewer;

    print(final_report);
}

Flow Diagram: 

    Researcher ────┐
                   │
                   ▼
               Reviewer ──► Final Output
                   ▲
                   │
    Analyst ───────┘

Use Cases: - Multi-source information aggregation - Expert consultation - Cross-validation

Conditional Branch Operator ??

Automatically select execution path based on input characteristics.

// Conditional branch operator
flow main {
    user_query = "URGENT: System outage detected";

    // Automatically select processing Agent based on input content
    handled = user_query ?? UrgentHandler : NormalHandler;

    print(handled);
}

Flow Diagram: 

         Input
          │
          ▼
    ┌─────────────┐
    │  Condition  │
    │  Judgment   │
    └─────────────┘
          │
    ┌─────┴─────┐
    │           │
    ▼           ▼
UrgentHandler  NormalHandler
    │           │
    └─────┬─────┘
          │
          ▼
        Output

Use Cases: - Urgency classification - Simple/complex task routing - Different request type handling

Fire-and-Forget Operator ||

Parallel execute multiple Agents, without waiting for results. Suitable for notifications, logging and other "fire-and-forget" scenarios.

// Fire-and-Forget operator
flow main {
    notification = "System maintenance scheduled at 2AM";

    // Parallel send notifications, not waiting for response
    notification || [EmailBot, SlackBot, SMSBot];

    // Main flow continues, not blocked
    print("Notifications sent, continuing with other tasks...");
}

Flow Diagram: 

         Input
           │
           ▼
    ┌──────┴──────┐
    │             │
    ▼             ▼
 EmailBot     SlackBot
    │             │
    ▼             ▼
 (Background)  (Background)

Use Cases: - Batch notification sending - Log recording - Non-critical task triggering

Consensus Operator &&

Parallel execute multiple Agents, wait for all results and perform voting/consensus decision.

// Consensus operator - Multi-expert voting decision
flow main {
    question = "Should we approve this loan application?";

    // Three experts parallel evaluate, wait for all results
    decision = question && [RiskAnalyst, CreditChecker, ComplianceOfficer];

    // decision contains all experts' opinions, can vote or comprehensive analysis
    print(decision);
}

Flow Diagram: 

              Input
                │
        ┌───────┼───────┐
        │       │       │
        ▼       ▼       ▼
    ExpertA  ExpertB  ExpertC
        │       │       │
        └───────┼───────┘
                │
                ▼
          Consensus Decision

Use Cases: - Multi-expert review - Voting decision systems - Cross-validation

DAG Operator Complete Comparison Table

| Operator | Name | Behavior | Use Case | | |-------|------|------|---------| | >> | Pipeline | Sequential passing | Unidirectional pipeline | | |>> | Fan-out | Parallel send to multiple Agents | Parallel processing | | &>> | Merge/Fan-in | Wait for multiple results then merge | Result integration | | ?? | Conditional Branch | Select path based on condition | Intelligent routing | | || | Fire-forget | Parallel execute without waiting | Async notification | | && | Consensus | Parallel execute wait for all results | Voting decision |

Combining DAG Operators

Build complex processing flows:

// From Nexa code example 15_dag_topology.nx
flow main {
    topic = "Quantum Computing business impact";

    // 1. Fork: topic is fed to Tech and Biz researchers for parallel analysis
    // 2. Merge: After both produce output, aggregate and send to Summarizer to write final report
    final_report = topic |>> [Researcher_Tech, Researcher_Biz] &>> Summarizer;

    // Branch routing: If report is abnormal, use backup bot; otherwise execute logging and dispatch
    final_report ?? RecoveryBot : Logger;
}

Complete Flow Diagram: 

                    topic
                      │
          ┌───────────┴───────────┐
          │                       │
          ▼                       ▼
   Researcher_Tech         Researcher_Biz
          │                       │
          └───────────┬───────────┘
                      │
                      ▼
                  Summarizer
                      │
                      ▼
              final_report
                      │
          ┌───────────┴───────────┐
          │                       │
          ▼                       ▼
    RecoveryBot               Logger
    (Exception)            (Normal)

DAG Operator Complete Example

// DAG topology example - Research report generation system
agent Researcher_Tech {
    role: "Technical Researcher",
    model: "deepseek/deepseek-chat",
    prompt: "Analyze quantum computing's technical level impact"
}

agent Researcher_Biz {
    role: "Business Researcher",
    model: "deepseek/deepseek-chat",
    prompt: "Analyze quantum computing's business level impact"
}

agent Summarizer {
    role: "Report Writer",
    model: "deepseek/deepseek-chat",
    prompt: "Integrate research results and write comprehensive report"
}

agent UrgentHandler {
    role: "Urgent Handler",
    prompt: "Quickly handle urgent issues"
}

agent NormalHandler {
    role: "Standard Handler",
    prompt: "Process according to standard procedures"
}

flow main {
    // Example 1: Simple pipeline
    simple_result = "What is AI?" >> Researcher_Tech >> Summarizer;

    // Example 2: Fork - Parallel processing
    parallel_results = "Quantum Computing" |>> [Researcher_Tech, Researcher_Biz];

    // Example 3: Merge - Integrate results
    merged_report = [Researcher_Tech, Researcher_Biz] &>> Summarizer;

    // Example 4: Conditional branch
    urgent_query = "URGENT: System crash!";
    handled = urgent_query ?? UrgentHandler : NormalHandler;

    // Example 5: Complex combination
    complex_flow = "AI trends 2024" 
        |>> [Researcher_Tech, Researcher_Biz] 
        &>> Summarizer;
}

🔁 Semantic Review and Reflection Loop: loop ... until

In many automated programming and long-text collaboration applications, the industry has concluded that a "reflective critic" mechanism must be adopted: i.e., Model A produces draft -> Model B acts as reviewer to correct errors -> Model A receives error feedback and rewrites.

How do traditional languages handle this logic? Developers need to hand-write an extremely fragile while True, then use a few awkward regex lines if "SUCCESS" in text: break praying the large model can precisely output the break word.

Nexa's philosophy is: Since even computation is done by large models, why can't judgment logic be natively returned to the semantic field? This gave birth to a language-level loop engine for semantic termination conditions.

Basic Syntax

loop {
    // Loop body
} until ("natural language termination condition")

Complete Example

flow main {
    // First step: initial production
    poem = Writer.run("Write a beautiful poem about AGI.");

    loop {
        // Use pipeline thinking to get critique feedback
        feedback = Critic.run(poem);

        // Use feedback as context guidance, rewrite in-place (Mutate)
        poem = Editor.run(poem, feedback);

    // Unique in the entire field: natural language judgment break condition
    } until ("The poem effectively rhymes and crucially mentions the technological singularity")
}

Preventing Infinite Loops

Use loop counter protection:

flow critic_pipeline(task: string) {
    loop {
        draft = Writer.run(task);
        feedback = Reviewer.run(draft);

    // Combine natural language semantic reasoning and programming strong logic interception (dual insurance mechanism)
    } until ("Code has exactly 0 bugs inside feedback" or runtime.meta.loop_count >= 4)

    // If hit the iteration limit, can throw manual interception exception
    if (runtime.meta.loop_count >= 4) {
        raise SuspendExecution("Reviewer and Writer entered deadlock. Need Human Check.");
    }
}

Loop Control Variables

Nexa provides the following built-in variables in loops:

Variable Description
runtime.meta.loop_count Current loop count
runtime.meta.last_result Result from last loop iteration

🔧 Semantic Conditional Judgment: semantic_if

Besides loop until, Nexa also provides semantic_if for semantic-level conditional judgment.

Basic Syntax

semantic_if "natural language condition" against input_variable {
    // Execute when condition is true
} else {
    // Execute when condition is false
}

Fast Match Mode

Use fast_match for regex pre-filtering to avoid unnecessary LLM calls:

semantic_if "contains specific date and location" fast_match r"\d{4}-\d{2}-\d{2}" against user_input {
    schedule_tool.run(user_input);
} else {
    print("Need further clarification");
}

How fast_match Works

  1. First check with regex quickly
  2. If regex matches, enter branch directly (saves tokens)
  3. If regex doesn't match, still call LLM for semantic judgment

Complete Example

flow main {
    user_input = '{"name": "Zhang San", "age": 25}';

    // Semantic conditional judgment - Determine if it's JSON
    semantic_if "Input content is valid JSON format" fast_match r"^\s*[\[{]" against user_input {
        result = JSONProcessor.run(user_input);
        print("Processed as JSON: " + result);
    } else {
        result = TextProcessor.run(user_input);
        print("Processed as text: " + result);
    }
}

🧩 Exception Handling: try/catch

Nexa v0.9.5 introduced native exception handling mechanism.

Basic Syntax

try {
    // Code that might error
} catch (error) {
    // Error handling
}

Complete Example

flow main {
    try {
        result = RiskyAgent.run("dangerous operation");
        print(result);
    } catch (error) {
        print("Error occurred: " + error);
        // Use fallback solution
        result = FallbackAgent.run("safe operation");
    }
}

📊 Chapter Summary

In this chapter, we learned Nexa's advanced orchestration features:

Feature Keyword Use Case
Pipeline Operation >> Agent chaining
Intent Routing match intent Request dispatching
Fork Operation |>> Parallel processing
Merge Operation &>> Result integration
Conditional Branch ?? Path selection
Fire-and-Forget || Async notification
Consensus && Voting decision
Semantic Loop loop until Iterative optimization
Semantic Condition semantic_if Intelligent judgment
Exception Handling try/catch Error handling

These features enable Nexa to elegantly handle the most complex agent orchestration scenarios, from simple pipelines to complex DAG topologies, from deterministic branching to semantic-level conditional judgment.

快来问问agent吧！

Nexa Agent

Nexa 文档助手

我是Nexa文档AI助手，可以问我有关文档的一切！

由AI Hub提供支持