3 min
ai
February 8, 2026

Agentic Loops and Orchestration Patterns

title: "Agentic Loops and Orchestration Patterns" description: "Research on AI agent workflow patterns, feedback loops, and orchestration strategies" date: 2026-02-06 topics: [agentic-ai, orchestration, workflows] sources: 5 status: initial

Agentic Loops and Orchestration Patterns

Executive Summary

Agentic workflows represent a paradigm shift from single-prompt AI interactions to autonomous, multi-step processes where AI agents act as orchestrators and integrators. Key patterns include feedback loops for continuous improvement and human-in-the-loop integration for critical decisions.

Key Concepts

1. The Agent Loop Pattern

The fundamental agent loop consists of:

  • Perceive: Gather context and inputs
  • Reason: Analyze and plan next steps
  • Act: Execute using available tools
  • Learn: Update state based on outcomes
┌─────────┐    ┌─────────┐    ┌─────────┐    ┌─────────┐
│ PERCEIVE│───▶│ REASON  │───▶│   ACT   │───▶│  LEARN  │
└─────────┘    └─────────┘    └─────────┘    └────┬────┘
     ▲───────────────────────────────────────────────┘

2. Orchestration Patterns

Agent as Orchestrator

  • Acts as glue unifying workflows
  • Accesses tools and integrates outputs
  • Reduces human intervention needed
  • Source: McKinsey, "One year of agentic AI"

Multi-Agent Collaboration

  • Multiple specialized agents working together
  • Workflow orchestrators managing handoffs
  • Collaborative systems for complex tasks
  • Source: AWS Prescriptive Guidance

3. Feedback Mechanisms

Continuous Improvement Loop

Execute → Measure → Analyze → Adjust → Execute

Human-in-the-Loop (HITL)

  • Facilitates agent decision-making
  • Steers agent output
  • Critical for high-stakes decisions
  • Source: IBM, "What are Agentic Workflows?"

Automated Feedback

  • Captures performance metrics
  • Evolves agent configurations
  • Enables self-improvement
  • Source: McKinsey, "Seizing the agentic AI advantage"

Implementation Patterns

Pattern 1: Tool-Augmented Agent

typescript
interface AgentLoop {
  context: Context;
  tools: Tool[];
  llm: LLM;
  
  async run(input: string): Promise<Result> {
    const plan = await this.llm.plan(input, this.context);
    for (const step of plan.steps) {
      const tool = this.selectTool(step);
      const result = await tool.execute(step.params);
      this.context.addObservation(result);
    }
    return this.llm.synthesize(this.context);
  }
}

Pattern 2: Nested Agent Hierarchy

typescript
interface Orchestrator {
  subAgents: Agent[];
  
  async delegate(task: Task): Promise<Result> {
    const agent = this.selectAgent(task.type);
    const subtasks = await this.decompose(task);
    const results = await Promise.all(
      subtasks.map(t => agent.execute(t))
    );
    return this.integrate(results);
  }
}

Pattern 3: Reflection Loop

typescript
interface ReflectiveAgent {
  async executeWithReflection(task: Task): Promise<Result> {
    let attempt = 0;
    let result = await this.execute(task);
    
    while (!this.validate(result) && attempt < MAX_RETRIES) {
      const reflection = await this.reflect(result.errors);
      result = await this.execute(task, reflection.feedback);
      attempt++;
    }
    
    return result;
  }
}

Observability Considerations

Metrics to Track

  • Loop iterations per task
  • Tool usage patterns
  • Error rates by step
  • Latency per phase
  • Cost per execution

Logging Strategy

  • Structured events for each loop phase
  • Context snapshots at key points
  • Tool call traces
  • Decision rationales

Reliability Patterns

1. Circuit Breaker

Prevent infinite loops by tracking:

  • Iteration count
  • Error frequency
  • Cost accumulation
  • Time elapsed

2. Checkpointing

Save state periodically to enable:

  • Recovery from failures
  • Resume after interruption
  • Audit trail creation

3. Graceful Degradation

When agent fails:

  • Fallback to simpler approach
  • Escalate to human
  • Return partial results

Sources

  1. McKinsey, "One year of agentic AI: Six lessons from the people doing the work" (Sept 2025)

  2. McKinsey, "Seizing the agentic AI advantage" (June 2025)

  3. IBM, "What are Agentic Workflows?" (Nov 2025)

  4. AWS Prescriptive Guidance, "Agentic AI patterns and workflows"

  5. Microsoft Azure, "AI Agent Orchestration Patterns"

Gaps and Opportunities

  • Standardized agent loop metrics
  • Interoperability between agent frameworks
  • Cost optimization strategies
  • Long-term memory management
  • Multi-agent coordination protocols