Building Multi-Agent Multimodal Collaboration Systems: A 2026 Production Guide with MCP Protocol

人工智能

Summary

  • Master the core architectural patterns of AI Agent multimodal collaboration, understanding the evolution path and design trade-offs from single-agent to multi-agent systems
  • Deep dive into MCP protocol tool invocation mechanisms, enabling unified orchestration and scheduling of text, image, and code multimodal capabilities
  • Production-grade multi-agent system implementation: task decomposition, context passing, conflict resolution, and fault tolerance across the entire pipeline

Table of Contents

1. Architectural Evolution of AI Agent Multimodal Collaboration

1.1 The Inevitable Shift from Monolithic Agents to Multi-Agent Collaboration

The AI Agent ecosystem in 2026 has fully evolved from the early single-model conversation paradigm to multi-agent multimodal collaboration architectures. This shift is not mere technical showmanship—it is driven by genuine production environment requirements. When enterprises deploy AI Agents to real business scenarios, a single Agent cannot meet complex task execution requirements in terms of capability boundaries, response latency, or fault tolerance.

The core bottlenecks of monolithic Agent architecture manifest in three dimensions: capability silos—a single model cannot simultaneously excel at text generation, image understanding, code writing, and data analysis; latency accumulation—complex tasks executed serially lead to linear growth in response time; single point of failure—one broken link crashes the entire chain. Multi-agent collaboration architectures fundamentally solve these problems through capability decoupling, parallel execution, and fault isolation.

1.2 Layered Architecture for Multimodal Collaboration

A mature multimodal collaboration system typically employs a four-layer architecture:

┌─────────────────────────────────────────────┐
│            Orchestration Layer               │
│   Task decomposition · Agent scheduling ·    │
│   Result aggregation · Conflict resolution   │
├─────────────────────────────────────────────┤
│            Capability Layer                  │
│  Text Agent · Image Agent · Code Agent ·     │
│  Data Agent                                  │
├─────────────────────────────────────────────┤
│            Tool Layer                        │
│  MCP Server · API Gateway · Local Tools ·    │
│  Sandbox Execution                           │
├─────────────────────────────────────────────┤
│            Infrastructure Layer              │
│  Model Inference · Vector Retrieval ·        │
│  Message Queue · State Storage               │
└─────────────────────────────────────────────┘

The orchestration layer serves as the "brain" of the entire AI Agent multimodal collaboration system, responsible for receiving user intent, decomposing subtasks, selecting appropriate Agents, managing execution flow, and aggregating final results. Each Agent in the capability layer focuses on a specific modality, interacting with the orchestration layer through standardized interfaces. The tool layer integrates external capabilities uniformly through the MCP protocol, while the infrastructure layer provides model inference and storage support.

1.3 Collaboration Pattern Classification

Three core patterns exist for multi-agent collaboration:

Sequential Collaboration: Tasks are passed to different Agents sequentially based on dependency relationships, suitable for pipeline scenarios with clear prerequisites. For example, "analyze document → extract data → generate report."

Parallel Collaboration: Multiple Agents process different subtasks simultaneously, suitable for subtasks without dependencies. For example, performing text summarization and image description generation concurrently, then merging results.

Negotiation Collaboration: Multiple Agents propose different solutions for the same problem, selecting the optimal solution through scoring or voting mechanisms. Suitable for high-uncertainty decision scenarios such as code review and security assessment.

2. MCP Protocol Deep Dive and Tool Orchestration

2.1 MCP Protocol Core Mechanisms

Model Context Protocol (MCP) is one of the most important standardization protocols in the 2025-2026 AI Agent ecosystem. It defines unified communication specifications between Agents and external tools, solving the problem of incompatible tool interfaces across different Agent frameworks.

Core concepts of the MCP protocol include:

  • MCP Server: The server side that encapsulates specific tool capabilities, exposing standardized tool descriptions and invocation interfaces
  • MCP Client: The Agent-side client responsible for discovering, selecting, and invoking tools provided by MCP Servers
  • Tool Schema: Uses JSON Schema to describe tool input/output formats, supporting type validation and auto-completion
  • Resource: Accessible resources exposed by MCP Servers (files, database records, etc.) that Agents can read on demand

A typical MCP tool definition:

{
  "name": "execute_sql_query",
  "description": "Execute a SQL query against the configured database and return results",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": {
        "type": "string",
        "description": "SQL query to execute"
      },
      "database": {
        "type": "string",
        "enum": ["analytics", "user_data", "logs"],
        "description": "Target database"
      },
      "limit": {
        "type": "integer",
        "default": 100,
        "description": "Maximum rows to return"
      }
    },
    "required": ["query", "database"]
  }
}

2.2 Multimodal Tool Orchestration Strategies

In AI Agent multimodal collaboration scenarios, the core challenge of tool orchestration is: how to automatically select appropriate tool combinations based on user intent and handle data flow between tools.

Intent-Based Tool Selection: The orchestration layer analyzes the semantics of user requests to match predefined tool combination templates. For example, when a user requests "analyze this sales chart and generate a report," the system automatically matches the combination of image understanding tool + data analysis tool + document generation tool.

Data Flow and Format Conversion: Different modality tools have varying input/output formats, requiring an intermediate conversion layer. Bounding box coordinates output by the image Agent need to be converted to table formats processable by the data Agent, and natural language descriptions generated by the text Agent need to be structured into parameters usable by the code Agent.

interface ToolOrchestrationPlan {
  taskId: string;
  steps: OrchestrationStep[];
  dataFlow: DataFlowEdge[];
  fallbackStrategy: FallbackStrategy;
}

interface OrchestrationStep {
  agentId: string;
  toolName: string;
  inputMapping: Record<string, string>;
  outputKey: string;
  timeout: number;
  retryPolicy: RetryPolicy;
}

interface DataFlowEdge {
  fromStep: string;
  toStep: string;
  transform: string;
  format: "json" | "base64" | "text" | "binary";
}

2.3 Production-Grade MCP Server Implementation

Building a production-grade MCP Server requires attention to three key areas: connection management, error handling, and performance monitoring.

from mcp.server import MCPServer, Tool
from mcp.types import ToolResult

server = MCPServer("data-analysis-tools")

@server.tool(
    name="analyze_dataset",
    description="Perform statistical analysis on a dataset",
    input_schema={
        "type": "object",
        "properties": {
            "dataset_path": {"type": "string"},
            "analysis_type": {
                "type": "string",
                "enum": ["descriptive", "correlation", "regression"]
            },
            "columns": {"type": "array", "items": {"type": "string"}}
        },
        "required": ["dataset_path", "analysis_type"]
    }
)
async def analyze_dataset(dataset_path: str, analysis_type: str,
                         columns: list[str] | None = None) -> ToolResult:
    try:
        import pandas as pd
        df = pd.read_csv(dataset_path)
        if columns:
            df = df[columns]
        match analysis_type:
            case "descriptive":
                result = df.describe().to_dict()
            case "correlation":
                result = df.corr().to_dict()
            case "regression":
                from sklearn.linear_model import LinearRegression
                model = LinearRegression()
                X = df.iloc[:, :-1].values
                y = df.iloc[:, -1].values
                model.fit(X, y)
                result = {"coefficients": model.coef_.tolist(),
                         "r_squared": model.score(X, y)}
        return ToolResult(content=[{"type": "text", "text": str(result)}])
    except Exception as e:
        return ToolResult(
            content=[{"type": "text", "text": f"Analysis failed: {str(e)}"}],
            isError=True
        )

if __name__ == "__main__":
    server.run(transport="stdio")

3. Core Design Patterns for Multi-Agent Systems

3.1 Agent Registration and Discovery

In multi-agent systems, the Agent registration and discovery mechanism is foundational infrastructure. Each Agent reports its capability description, supported modality types, current load status, and health check endpoints to the registry upon startup.

use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AgentProfile {
    pub agent_id: String,
    pub capabilities: Vec<ModalityCapability>,
    pub supported_tools: Vec<String>,
    pub max_concurrent_tasks: u32,
    pub current_load: f64,
    pub health_endpoint: String,
    pub version: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModalityCapability {
    pub modality: ModalityType,
    pub confidence: f64,
    pub avg_latency_ms: u64,
    pub cost_per_invocation: f64,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModalityType {
    TextGeneration,
    ImageUnderstanding,
    CodeGeneration,
    DataAnalysis,
    AudioProcessing,
    VideoUnderstanding,
}

pub struct AgentRegistry {
    agents: DashMap<String, AgentProfile>,
}

impl AgentRegistry {
    pub fn find_best_agent(&self, requirement: &TaskRequirement) -> Option<AgentProfile> {
        self.agents
            .iter()
            .filter(|entry| {
                let profile = entry.value();
                profile.capabilities.iter().any(|cap| {
                    cap.modality == requirement.modality
                        && cap.confidence >= requirement.min_confidence
                }) && profile.current_load < 0.9
            })
            .min_by(|a, b| {
                let score_a = self.compute_agent_score(a.value(), requirement);
                let score_b = self.compute_agent_score(b.value(), requirement);
                score_b.partial_cmp(&score_a).unwrap()
            })
            .map(|entry| entry.value().clone())
    }
}

3.2 Task Decomposition and Assignment Strategies

Task decomposition is the starting point of multi-agent collaboration. The orchestration Agent needs to break down a user's complex request into independently executable subtasks and determine the dependency relationships between subtasks.

Hierarchical Decomposition: First decompose at a coarse granularity by business domain, then refine within each domain. For example, "create a quarterly product analysis report" → decomposed into "data collection," "chart generation," "text writing," and "layout integration" domain tasks, each further refined into specific substeps.

Dependency Graph Construction: Decomposed subtasks are organized into a Directed Acyclic Graph (DAG), where nodes are subtasks and edges are data dependency relationships. The topological sort of the DAG determines task execution order, and nodes without dependencies can execute in parallel.

type TaskNode struct {
    ID           string
    AgentType    string
    InputSchema  map[string]interface{}
    Dependencies []string
    Priority     int
    Timeout      time.Duration
    RetryCount   int
}

type TaskDAG struct {
    nodes    map[string]*TaskNode
    edges    map[string][]string
    mu       sync.RWMutex
}

func (dag *TaskDAG) TopologicalSort() ([]string, error) {
    dag.mu.RLock()
    defer dag.mu.RUnlock()

    inDegree := make(map[string]int)
    for id := range dag.nodes {
        inDegree[id] = 0
    }
    for _, deps := range dag.edges {
        for _, dep := range deps {
            inDegree[dep]++
        }
    }

    var queue []string
    for id, degree := range inDegree {
        if degree == 0 {
            queue = append(queue, id)
        }
    }

    var sorted []string
    for len(queue) > 0 {
        current := queue[0]
        queue = queue[1:]
        sorted = append(sorted, current)

        for neighbor, deps := range dag.edges {
            for i, dep := range deps {
                if dep == current {
                    dag.edges[neighbor] = append(deps[:i], deps[i+1:]...)
                    inDegree[neighbor]--
                    if inDegree[neighbor] == 0 {
                        queue = append(queue, neighbor)
                    }
                    break
                }
            }
        }
    }

    if len(sorted) != len(dag.nodes) {
        return nil, fmt.Errorf("cycle detected in task DAG")
    }
    return sorted, nil
}

3.3 Inter-Agent Communication Protocols

The communication pattern in multi-agent systems determines the efficiency and reliability of collaboration. Mainstream approaches include:

Synchronous Request-Response: Suitable for simple tool invocation scenarios where Agent A directly calls Agent B's capability and waits for the return. Simple to implement but highly coupled.

Asynchronous Message Passing: Decouples direct dependencies between Agents through message queues. Agents publish tasks to Topics, and Agents subscribed to those Topics consume and process them. Supports peak shaving and fault isolation.

Shared State Space: All Agents read and write to the same state store (e.g., Redis), sensing state changes through Watch mechanisms. Suitable for scenarios requiring real-time synchronization of intermediate results.

Production environments recommend a hybrid pattern: critical paths use synchronous calls to ensure low latency, non-critical paths use asynchronous messaging for decoupling, and shared state space is used for cross-Agent context passing.

4. Multimodal Context Management and State Synchronization

4.1 Context Window Challenges

One of the greatest technical challenges in AI Agent multimodal collaboration is context management. During multi-agent collaboration, each Agent needs to understand the global context to make correct decisions, but large model context windows are limited, making it impossible to stuff all historical information into a single request.

Context Compression Strategies:

  1. Summary Compression: Perform hierarchical summarization of historical conversations and intermediate results, preserving key decision nodes and important data
  2. Sliding Window: Only retain complete context for the most recent N rounds of interaction; earlier information is retained only as summaries
  3. Selective Injection: Dynamically select context fragments to inject based on relevance scoring to the current task
class ContextManager:
    def __init__(self, max_tokens: int = 8000):
        self.max_tokens = max_tokens
        self.conversation_history: list[Message] = []
        self.summaries: list[Summary] = []
        self.artifact_store: dict[str, Artifact] = {}

    def build_context(self, current_task: Task) -> list[Message]:
        budget = self.max_tokens
        context: list[Message] = []

        recent_messages = self._get_recent_messages(budget // 2)
        budget -= sum(m.token_count for m in recent_messages)
        context.extend(recent_messages)

        if budget > 500:
            relevant_summaries = self._rank_summaries(current_task, budget // 3)
            for summary in relevant_summaries:
                if summary.token_count <= budget:
                    context.append(summary.to_message())
                    budget -= summary.token_count

        if budget > 200:
            relevant_artifacts = self._find_relevant_artifacts(current_task)
            for artifact in relevant_artifacts:
                artifact_msg = artifact.to_compact_message(budget)
                if artifact_msg:
                    context.append(artifact_msg)
                    budget -= artifact_msg.token_count

        return context

4.2 Multimodal State Synchronization Mechanisms

During parallel multi-agent collaboration, different Agents may simultaneously modify shared state, requiring conflict resolution. Operational Transformation (OT) or CRDT (Conflict-free Replicated Data Types) are used to ensure eventual consistency.

For AI Agent scenarios, CRDT is more suitable because Agent operations are typically append-only (adding analysis results, appending generated content), naturally fitting G-Counter, LWW-Register, and other CRDT data structures.

interface AgentState {
  artifacts: LWWMap<Artifact>;
  taskProgress: GCounter;
  decisions: LWWRegister<Decision>;
}

class LWWMap<T> {
  private entries: Map<string, { value: T; timestamp: number; agentId: string }>;

  set(key: string, value: T, timestamp: number, agentId: string): void {
    const existing = this.entries.get(key);
    if (!existing || timestamp > existing.timestamp) {
      this.entries.set(key, { value, timestamp, agentId });
    }
  }

  merge(other: LWWMap<T>): void {
    for (const [key, entry] of other.entries) {
      this.set(key, entry.value, entry.timestamp, entry.agentId);
    }
  }
}

class GCounter {
  private counts: Map<string, number>;

  increment(agentId: string, delta: number = 1): void {
    const current = this.counts.get(agentId) ?? 0;
    this.counts.set(agentId, current + delta);
  }

  value(): number {
    let total = 0;
    for (const count of this.counts.values()) {
      total += count;
    }
    return total;
  }

  merge(other: GCounter): void {
    for (const [agentId, count] of other.counts) {
      const current = this.counts.get(agentId) ?? 0;
      this.counts.set(agentId, Math.max(current, count));
    }
  }
}

4.3 Cross-Modal Alignment and Fusion

The ultimate goal of multimodal collaboration is to achieve semantic alignment of information across different modalities. The "upward trend" described by the text Agent needs to be semantically consistent with the line chart generated by the image Agent, and the statistical metrics calculated by the data Agent need to match the conclusions in the text report.

Alignment Strategy: Use a shared embedding space to map outputs from different modalities into the same vector space, detecting cross-modal semantic consistency through cosine similarity. When consistency falls below a threshold, trigger negotiation collaboration mode to have relevant Agents recalibrate their outputs.

5. Production-Grade Agent Scheduling Engine Implementation

5.1 Scheduling Engine Architecture

A production-grade Agent scheduling engine needs to handle high-concurrency requests, dynamic Agent pool management, and complex task dependency relationships. Below is the core scheduling engine logic implemented in Go:

package scheduler

type Scheduler struct {
    registry    *AgentRegistry
    taskQueue   chan *Task
    resultStore ResultStore
    maxWorkers  int
    wg          sync.WaitGroup
}

type Task struct {
    ID           string
    Requirement  TaskRequirement
    DAG          *TaskDAG
    Context      TaskContext
    Priority     int
    SubmittedAt  time.Time
    Deadline     time.Time
}

func NewScheduler(registry *AgentRegistry, store ResultStore, maxWorkers int) *Scheduler {
    return &Scheduler{
        registry:    registry,
        taskQueue:   make(chan *Task, maxWorkers*2),
        resultStore: store,
        maxWorkers:  maxWorkers,
    }
}

func (s *Scheduler) Start(ctx context.Context) {
    for i := 0; i < s.maxWorkers; i++ {
        s.wg.Add(1)
        go s.worker(ctx, i)
    }
}

func (s *Scheduler) worker(ctx context.Context, id int) {
    defer s.wg.Done()
    for {
        select {
        case <-ctx.Done():
            return
        case task := <-s.taskQueue:
            result, err := s.executeTask(ctx, task)
            if err != nil {
                s.resultStore.Store(task.ID, Result{Error: err.Error()})
                continue
            }
            s.resultStore.Store(task.ID, *result)
        }
    }
}

5.2 Priority and Fair Scheduling

In production environments, task priorities vary significantly. VIP customer requests need prioritized processing, while batch background tasks can be delayed. The scheduling engine needs to implement a Weighted Fair Queuing (WFQ) algorithm, ensuring rapid response for high-priority tasks while preventing low-priority task starvation.

5.3 Flow Control and Backpressure

When system load is excessively high, the scheduling engine needs to implement backpressure mechanisms to prevent cascading failures. Core strategies include:

  • Token Bucket Rate Limiting: Configure different QPS limits per Agent type
  • Adaptive Degradation: Automatically skip non-critical steps when queue backlog exceeds thresholds
  • Circuit Breaker: Agents with N consecutive failed calls are automatically circuit-broken, with periodic probe recovery

6. Fault Tolerance, Observability, and Security Governance

6.1 Multi-Layer Fault Tolerance

Fault tolerance in AI Agent multimodal collaboration systems needs to be designed at three levels:

Agent-Level Fault Tolerance: When a single Agent fails, the scheduling engine automatically switches to a backup Agent or degrades to a simplified process. Agent status is sensed in real-time through health checks and heartbeat mechanisms.

Task-Level Fault Tolerance: When task execution fails, choose retry, degradation, or rollback based on the failure reason. Critical business tasks require Checkpoint mechanisms to support recovery from intermediate states.

System-Level Fault Tolerance: The entire orchestration service requires high-availability deployment, ensuring service continuity through primary-backup switching and state recovery.

class CircuitBreaker:
    def __init__(self, failure_threshold: int = 5, recovery_timeout: float = 30.0):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.failure_count = 0
        self.last_failure_time: float | None = None
        self.state = "closed"

    def call(self, func, *args, **kwargs):
        if self.state == "open":
            if time.time() - self.last_failure_time > self.recovery_timeout:
                self.state = "half_open"
            else:
                raise CircuitBreakerOpenError("Circuit breaker is open")

        try:
            result = func(*args, **kwargs)
            if self.state == "half_open":
                self.state = "closed"
                self.failure_count = 0
            return result
        except Exception as e:
            self.failure_count += 1
            self.last_failure_time = time.time()
            if self.failure_count >= self.failure_threshold:
                self.state = "open"
            raise

6.2 Observability System

Observability in multi-agent systems needs to cover three dimensions:

Distributed Tracing: Each task is assigned a unique Trace ID, and subtasks inherit the parent task's Trace ID and generate Span IDs, enabling full-chain tracing. OpenTelemetry is the recommended standard.

Metrics Monitoring: Core metrics include Agent invocation latency P50/P95/P99, task success rate, queue depth, Agent utilization, and Token consumption.

Structured Logging: All Agent operations are recorded as structured logs containing task ID, Agent ID, operation type, input summary, output summary, duration, and error information.

6.3 Security Governance

Security governance for AI Agent multimodal collaboration requires attention to:

  • Tool Permission Control: Each Agent can only invoke tools required by its role, following the principle of least privilege
  • Data Masking: Sensitive data passed between Agents must be automatically masked to prevent privacy leaks
  • Audit Logging: All Agent operations leave traces, supporting post-hoc auditing and compliance checks
  • Sandbox Execution: Code execution Agents must run in sandboxed environments with restricted file system and network access

7. Enterprise Deployment and Performance Tuning

7.1 Kubernetes Deployment

Deploying AI Agent multimodal collaboration systems on Kubernetes requires considering the differentiated needs of three components: orchestration services, Agent Workers, and MCP Servers.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: agent-orchestrator
  namespace: ai-agents
spec:
  replicas: 3
  selector:
    matchLabels:
      app: agent-orchestrator
  template:
    metadata:
      labels:
        app: agent-orchestrator
    spec:
      containers:
      - name: orchestrator
        image: registry.example.com/agent-orchestrator:v2.1.0
        ports:
        - containerPort: 8080
        resources:
          requests:
            cpu: "2"
            memory: "4Gi"
          limits:
            cpu: "4"
            memory: "8Gi"
        env:
        - name: REDIS_URL
          valueFrom:
            secretKeyRef:
              name: agent-secrets
              key: redis-url
        - name: AGENT_POOL_SIZE
          value: "50"
        livenessProbe:
          httpGet:
            path: /health
            port: 8080
          initialDelaySeconds: 10
          periodSeconds: 15
        readinessProbe:
          httpGet:
            path: /ready
            port: 8080
          initialDelaySeconds: 5
          periodSeconds: 10
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: agent-orchestrator-hpa
  namespace: ai-agents
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: agent-orchestrator
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Pods
    pods:
      metric:
        name: task_queue_depth
      target:
        type: AverageValue
        averageValue: "10"

7.2 Key Performance Tuning Parameters

Parameter Recommended Value Description
Orchestration service concurrency CPU cores × 2 Fully utilize multi-core
Agent Worker pool size 50-200 Adjust based on Agent type
Task queue depth Worker pool × 2 Prevent queue overflow
Context compression threshold 6000 tokens Reserve space for tool calls
MCP connection pool size 20-50 Avoid frequent connection setup
Circuit breaker recovery time 30-60 seconds Balance recovery speed and safety

7.3 Cost Optimization

The cost of AI Agent multimodal collaboration systems primarily comes from model inference calls. Optimization strategies include:

  • Model Tiering: Use small models (e.g., Qwen3-4B) for simple tasks and large models (e.g., Qwen3-72B) for complex tasks
  • Cache Reuse: Cache inference results for similar requests to avoid duplicate calls
  • Batch Processing: Combine multiple small tasks into batch requests to reduce per-call costs
  • Asynchronous Pre-computation: Predictively load outputs for commonly used tools, reducing real-time inference volume

8. Conclusion and Future Outlook

AI Agent multimodal collaboration architecture is the core direction of intelligent agent system development in 2026. This article systematically elaborates on the construction methods for production-grade multi-agent systems from seven dimensions: architectural evolution, MCP protocol, multi-agent design patterns, context management, scheduling engines, fault tolerance and security, and deployment tuning.

Key takeaways:

  1. Architecture Selection: Choose sequential, parallel, or negotiation collaboration patterns based on business complexity; hybrid patterns are best practice for production environments
  2. MCP Protocol: Standardized tool integration is the foundation for Agent ecosystem prosperity; prioritize MCP over custom tool protocols
  3. Context Management: Summary compression + selective injection + CRDT state synchronization form the core technology stack for multimodal collaboration
  4. Scheduling Engine: WFQ priority scheduling + backpressure rate limiting + circuit breaker degradation ensure system stability under high load
  5. Security Governance: Least privilege + data masking + sandbox execution + audit logging are necessary conditions for enterprise deployment

Looking ahead, as the MCP protocol ecosystem matures and more modality capabilities are integrated, AI Agent multimodal collaboration will evolve from the current "tool orchestration" mode to a true "autonomous collaboration" mode—where Agents can autonomously negotiate task allocation, dynamically adjust collaboration strategies, and continuously learn to optimize execution efficiency.

References

#AI Agent#多模态协作#大模型Agent#MCP协议#Agent框架#2026