Multi-agent AI systems are the most overhyped and simultaneously most underestimated pattern in production AI right now. The demos look spectacular -- agents collaborating, delegating, reasoning together. The reality is different. Most multi-agent deployments we have seen in the wild either (a) quietly got replaced by a single well-prompted agent, or (b) required months of debugging to get to 90% reliability.

At CODERCOPS, we have shipped multi-agent systems for client projects involving document processing pipelines, customer support automation, and code review workflows. Some of these genuinely needed multiple agents. Most did not. This post covers both sides: when to use multi-agent, and how to build it when you actually need it.

AI Agent Teams Multi-agent systems require careful orchestration to work in production

The Honest Truth: You Probably Don't Need Multiple Agents

Before we get into architecture patterns, we need to address the elephant in the room. The majority of tasks that teams try to solve with multi-agent systems can be solved better with a single agent that has good tools and clear instructions.

Here is our decision framework:

Should you use multiple agents?

START
  │
  ├── Does the task require multiple distinct skill sets
  │   that cannot be captured in a single system prompt?
  │   │
  │   ├── NO → Use a single agent with tools. Stop here.
  │   │
  │   └── YES
  │       │
  │       ├── Do the subtasks need to run in parallel
  │       │   for latency reasons?
  │       │   │
  │       │   ├── YES → Consider parallel multi-agent.
  │       │   │
  │       │   └── NO
  │       │       │
  │       │       ├── Can you chain the subtasks with
  │       │       │   deterministic handoff logic?
  │       │       │   │
  │       │       │   ├── YES → Use a pipeline (not agents).
  │       │       │   │
  │       │       │   └── NO → Multi-agent may be justified.
  │       │       │
  │       │       └── Is the total context too large for
  │       │           a single agent's window?
  │       │           │
  │       │           ├── YES → Multi-agent with context splitting.
  │       │           │
  │       │           └── NO → Single agent. Seriously.
  │
  └── END

In our experience, roughly 70% of the "multi-agent" projects that come to us end up being single-agent solutions with better tool design. The remaining 30% genuinely benefit from multiple agents. This post is about that 30%.

Orchestration Patterns

There are four fundamental patterns for organizing multi-agent systems. Every production deployment we have built uses one of these (or a combination).

Pattern 1: Sequential Pipeline

Agents execute in a fixed order, each one processing and passing results to the next.

┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐
│  Agent 1  │────►│  Agent 2  │────►│  Agent 3  │────►│  Agent 4  │
│ (Extract) │     │ (Analyze) │     │ (Generate)│     │ (Review)  │
└──────────┘     └──────────┘     └──────────┘     └──────────┘
     │                │                │                │
     ▼                ▼                ▼                ▼
  Raw data      Structured data   Draft output    Final output

When to use: Document processing, content generation pipelines, data transformation chains.

Production example: We built a contract analysis pipeline for a legal tech client:

  1. Extraction Agent -- Reads the PDF, extracts clauses, parties, dates, and obligations using structured output.
  2. Analysis Agent -- Compares extracted terms against the client's standard templates, flags deviations.
  3. Summary Agent -- Generates a human-readable risk summary with recommendations.
  4. Review Agent -- Validates the summary against the original document, catches hallucinations.
interface PipelineStage<TInput, TOutput> {
  name: string;
  agent: Agent;
  process: (input: TInput) => Promise<TOutput>;
  validate: (output: TOutput) => Promise<boolean>;
  maxRetries: number;
}

async function runPipeline<T>(
  stages: PipelineStage<unknown, unknown>[],
  initialInput: T
): Promise<unknown> {
  let currentInput: unknown = initialInput;

  for (const stage of stages) {
    let attempts = 0;
    let output: unknown;

    while (attempts < stage.maxRetries) {
      attempts++;
      log("info", `Running ${stage.name}, attempt ${attempts}`);

      try {
        output = await stage.process(currentInput);

        if (await stage.validate(output)) {
          log("info", `${stage.name} completed successfully`);
          break;
        }

        log("warn", `${stage.name} validation failed, retrying`);
      } catch (err) {
        log("error", `${stage.name} error`, {
          error: (err as Error).message,
          attempt: attempts,
        });

        if (attempts >= stage.maxRetries) {
          throw new PipelineError(
            `${stage.name} failed after ${attempts} attempts`,
            stage.name,
            err as Error
          );
        }
      }
    }

    currentInput = output;
  }

  return currentInput;
}

Pattern 2: Parallel Fan-Out

Multiple agents work on different aspects of the same input simultaneously, and a coordinator merges their results.

                    ┌──────────────┐
                    │  Coordinator  │
                    │    Agent      │
                    └──────┬───────┘
                           │
              ┌────────────┼────────────┐
              │            │            │
              ▼            ▼            ▼
       ┌──────────┐ ┌──────────┐ ┌──────────┐
       │ Agent A   │ │ Agent B   │ │ Agent C   │
       │ (Security │ │ (Perf.   │ │ (Style   │
       │  Review)  │ │  Review) │ │  Review) │
       └─────┬────┘ └─────┬────┘ └─────┬────┘
             │            │            │
             └────────────┼────────────┘
                          │
                          ▼
                   ┌──────────────┐
                   │   Merger      │
                   │   Agent       │
                   └──────────────┘
                          │
                          ▼
                   Combined Report

When to use: When subtasks are independent and latency matters. Code review, multi-dimensional analysis, parallel research.

Implementation:

interface ParallelTask {
  name: string;
  agent: Agent;
  systemPrompt: string;
}

async function fanOutFanIn(
  tasks: ParallelTask[],
  input: string,
  mergerAgent: Agent
): Promise<string> {
  // Fan out: run all tasks in parallel
  const results = await Promise.allSettled(
    tasks.map(async (task) => {
      const startTime = Date.now();
      try {
        const result = await task.agent.run({
          system: task.systemPrompt,
          messages: [{ role: "user", content: input }],
        });
        return {
          name: task.name,
          result: result.content,
          latencyMs: Date.now() - startTime,
          status: "success" as const,
        };
      } catch (err) {
        return {
          name: task.name,
          result: `Error: ${(err as Error).message}`,
          latencyMs: Date.now() - startTime,
          status: "error" as const,
        };
      }
    })
  );

  // Collect results, handling failures gracefully
  const collected = results.map((r, i) => {
    if (r.status === "fulfilled") return r.value;
    return {
      name: tasks[i].name,
      result: `Agent failed: ${r.reason}`,
      latencyMs: 0,
      status: "error" as const,
    };
  });

  // Fan in: merge results
  const mergePrompt = `
You are merging results from ${collected.length} parallel analyses.
Combine them into a single coherent report, noting any conflicts.

${collected
  .map(
    (c) => `
## ${c.name} (${c.status})
${c.result}
`
  )
  .join("\n")}
`;

  const merged = await mergerAgent.run({
    messages: [{ role: "user", content: mergePrompt }],
  });

  return merged.content;
}

Pattern 3: Hierarchical Delegation

A supervisor agent breaks down tasks and delegates to specialist agents, then synthesizes their outputs.

                     ┌──────────────────┐
                     │   Supervisor      │
                     │   Agent           │
                     │                   │
                     │  - Understands    │
                     │    full task      │
                     │  - Delegates      │
                     │  - Synthesizes    │
                     └────────┬─────────┘
                              │
            ┌─────────────────┼─────────────────┐
            │                 │                 │
            ▼                 ▼                 ▼
     ┌─────────────┐  ┌─────────────┐  ┌─────────────┐
     │  Research    │  │  Coding     │  │  Testing    │
     │  Agent       │  │  Agent      │  │  Agent      │
     │             │  │             │  │             │
     │  Tools:     │  │  Tools:     │  │  Tools:     │
     │  - web      │  │  - editor   │  │  - runner   │
     │    search   │  │  - terminal │  │  - coverage │
     │  - docs     │  │  - git      │  │  - lint     │
     └─────────────┘  └─────────────┘  └─────────────┘

When to use: Complex, open-ended tasks where subtask decomposition itself requires intelligence. Software development, research projects, multi-step customer requests.

This is the most powerful pattern and the hardest to get right. The supervisor agent needs to:

  1. Decompose the task intelligently
  2. Assign to the right specialist
  3. Evaluate whether each specialist's output is good enough
  4. Decide when to re-delegate vs move forward
  5. Synthesize everything coherently
interface SpecialistAgent {
  name: string;
  description: string;
  agent: Agent;
  tools: Tool[];
}

class SupervisorOrchestrator {
  private supervisor: Agent;
  private specialists: Map<string, SpecialistAgent>;
  private conversationHistory: Message[] = [];
  private tokenBudget: number;
  private tokensUsed: number = 0;

  constructor(config: {
    supervisor: Agent;
    specialists: SpecialistAgent[];
    tokenBudget: number;
  }) {
    this.supervisor = config.supervisor;
    this.specialists = new Map(
      config.specialists.map((s) => [s.name, s])
    );
    this.tokenBudget = config.tokenBudget;
  }

  async execute(task: string): Promise<string> {
    // Step 1: Supervisor creates a plan
    const plan = await this.supervisor.run({
      system: this.buildSupervisorPrompt(),
      messages: [
        {
          role: "user",
          content: `Task: ${task}\n\nCreate a plan by specifying which specialists to use and in what order.`,
        },
      ],
      tools: [this.delegateTool(), this.completeTool()],
    });

    // Step 2: Execute plan via tool calls
    // The supervisor uses delegate_to_specialist tool calls
    // to assign work, and the orchestrator routes them

    return this.conversationHistory
      .filter((m) => m.role === "assistant")
      .pop()?.content || "No output produced";
  }

  private delegateTool(): Tool {
    return {
      name: "delegate_to_specialist",
      description: "Delegate a subtask to a specialist agent",
      parameters: {
        specialist: {
          type: "string",
          enum: Array.from(this.specialists.keys()),
        },
        task: { type: "string" },
        context: { type: "string" },
      },
      execute: async ({ specialist, task, context }) => {
        const spec = this.specialists.get(specialist);
        if (!spec) throw new Error(`Unknown specialist: ${specialist}`);

        // Budget check
        if (this.tokensUsed > this.tokenBudget * 0.9) {
          return "Token budget nearly exhausted. Please synthesize current results.";
        }

        const result = await spec.agent.run({
          messages: [
            {
              role: "user",
              content: `${task}\n\nContext: ${context}`,
            },
          ],
          tools: spec.tools,
        });

        this.tokensUsed += result.usage.totalTokens;
        return result.content;
      },
    };
  }

  private buildSupervisorPrompt(): string {
    const specialistDescriptions = Array.from(this.specialists.values())
      .map((s) => `- ${s.name}: ${s.description}`)
      .join("\n");

    return `You are a supervisor agent. You decompose complex tasks and delegate to specialists.

Available specialists:
${specialistDescriptions}

Rules:
- Break the task into clear subtasks
- Delegate each subtask to the most appropriate specialist
- Review each specialist's output before proceeding
- If output is insufficient, re-delegate with more specific instructions
- When all subtasks are complete, synthesize a final result
- Stay within the token budget (${this.tokenBudget} tokens, ${this.tokensUsed} used so far)`;
  }
}

Pattern 4: Debate / Adversarial

Two or more agents argue different positions, and a judge agent evaluates. Useful for quality control and decision-making.

┌──────────┐          ┌──────────┐
│ Agent A   │◄────────►│ Agent B   │
│ (Propose) │  debate  │ (Critique)│
└─────┬────┘          └─────┬────┘
      │                     │
      └──────────┬──────────┘
                 │
                 ▼
          ┌──────────────┐
          │  Judge Agent  │
          │  (Evaluate)   │
          └──────────────┘
                 │
                 ▼
          Final Decision

When to use: High-stakes decisions, content quality validation, security review.

We use this pattern for one specific purpose: catching hallucinations. Agent A generates a response, Agent B tries to find factual errors in it, and a Judge decides what to keep.

Agent-to-Agent Communication

The single hardest problem in multi-agent systems is not building individual agents -- it is making them communicate effectively. Here are the patterns that work.

Structured Message Passing

Never pass raw text between agents. Use structured formats:

interface AgentMessage {
  from: string;
  to: string;
  type:
    | "task_assignment"
    | "result"
    | "clarification_request"
    | "error";
  payload: {
    content: string;
    metadata: Record<string, unknown>;
    confidence?: number; // 0-1, how confident the agent is
  };
  timestamp: string;
  parentMessageId?: string;
}

// Example: Research agent returning results
const message: AgentMessage = {
  from: "research-agent",
  to: "supervisor",
  type: "result",
  payload: {
    content: "Found 3 relevant pricing models...",
    metadata: {
      sourcesChecked: 12,
      sourcesRelevant: 3,
      searchQueries: [
        "SaaS pricing models 2026",
        "usage-based pricing benchmarks",
      ],
    },
    confidence: 0.85,
  },
  timestamp: new Date().toISOString(),
  parentMessageId: "task-001",
};

Shared Context Store

For complex workflows, agents need shared state. We use a simple key-value store with namespacing:

class SharedContext {
  private store = new Map<string, unknown>();
  private accessLog: Array<{
    agent: string;
    key: string;
    operation: "read" | "write";
    timestamp: number;
  }> = [];

  write(agent: string, key: string, value: unknown): void {
    this.store.set(key, value);
    this.accessLog.push({
      agent,
      key,
      operation: "write",
      timestamp: Date.now(),
    });
  }

  read(agent: string, key: string): unknown {
    this.accessLog.push({
      agent,
      key,
      operation: "read",
      timestamp: Date.now(),
    });
    return this.store.get(key);
  }

  // Get a summary of what's in context (for agent prompts)
  getSummary(): string {
    const keys = Array.from(this.store.keys());
    return keys
      .map((k) => {
        const val = this.store.get(k);
        const preview =
          typeof val === "string"
            ? val.slice(0, 100)
            : JSON.stringify(val).slice(0, 100);
        return `- ${k}: ${preview}...`;
      })
      .join("\n");
  }
}

Error Handling and Retry Strategies

In a single-agent system, an error means one retry. In a multi-agent system, errors cascade. Here is how we handle them.

The Retry Hierarchy

Level 1: Tool Retry
  └── A tool call fails (API timeout, rate limit)
      └── Retry the same tool call with exponential backoff
          └── Max 3 attempts

Level 2: Agent Retry
  └── An agent produces invalid output
      └── Re-run the agent with the error as context
          └── Max 2 attempts

Level 3: Pipeline Retry
  └── A full pipeline stage fails after agent retries
      └── Re-run the stage with fresh agent instances
          └── Max 1 attempt

Level 4: Graceful Degradation
  └── Pipeline retry fails
      └── Return partial results with error context
          └── Human review queue
class RetryPolicy {
  async withRetry<T>(
    operation: () => Promise<T>,
    config: {
      maxAttempts: number;
      backoffMs: number;
      backoffMultiplier: number;
      onRetry?: (attempt: number, error: Error) => void;
    }
  ): Promise<T> {
    let lastError: Error | undefined;
    let delay = config.backoffMs;

    for (let attempt = 1; attempt <= config.maxAttempts; attempt++) {
      try {
        return await operation();
      } catch (err) {
        lastError = err as Error;
        config.onRetry?.(attempt, lastError);

        if (attempt < config.maxAttempts) {
          await new Promise((resolve) => setTimeout(resolve, delay));
          delay *= config.backoffMultiplier;
        }
      }
    }

    throw lastError;
  }
}

// Usage
const retryPolicy = new RetryPolicy();

const result = await retryPolicy.withRetry(
  () => agent.run({ messages: [{ role: "user", content: task }] }),
  {
    maxAttempts: 3,
    backoffMs: 1000,
    backoffMultiplier: 2,
    onRetry: (attempt, error) => {
      log("warn", `Agent retry ${attempt}`, {
        error: error.message,
      });
    },
  }
);

Circuit Breaker for External Services

When an agent depends on an external API that is down, retrying just wastes tokens:

class CircuitBreaker {
  private failures = 0;
  private lastFailure = 0;
  private state: "closed" | "open" | "half-open" = "closed";

  constructor(
    private threshold: number = 5,
    private resetTimeMs: number = 60000
  ) {}

  async execute<T>(operation: () => Promise<T>): Promise<T> {
    if (this.state === "open") {
      if (Date.now() - this.lastFailure > this.resetTimeMs) {
        this.state = "half-open";
      } else {
        throw new Error(
          "Circuit breaker is open. The external service is unavailable. Try again later."
        );
      }
    }

    try {
      const result = await operation();
      this.failures = 0;
      this.state = "closed";
      return result;
    } catch (err) {
      this.failures++;
      this.lastFailure = Date.now();
      if (this.failures >= this.threshold) {
        this.state = "open";
      }
      throw err;
    }
  }
}

Monitoring and Observability

You cannot debug what you cannot see. Multi-agent systems require more observability than any other architecture pattern we work with.

What to Track

Metric Why Alert Threshold
Total tokens per workflow Cost control > 2x expected
Tokens per agent Identify chatty agents > budget allocation
Latency per agent Performance bottlenecks > 30s for any agent
Total workflow latency User experience > 2min for interactive
Retry count per agent Reliability issues > 2 retries per run
Error rate per agent Failing components > 10%
Delegation depth Infinite loops > 5 levels
Confidence scores Quality tracking < 0.6 average

Structured Trace Logging

Every agent interaction gets a trace:

interface AgentTrace {
  traceId: string;
  workflowId: string;
  agentName: string;
  parentTraceId?: string;
  startTime: number;
  endTime?: number;
  input: {
    messageCount: number;
    estimatedTokens: number;
  };
  output?: {
    contentLength: number;
    toolCalls: number;
    tokensUsed: number;
  };
  error?: {
    message: string;
    retryAttempt: number;
  };
  metadata: Record<string, unknown>;
}

class TraceCollector {
  private traces: AgentTrace[] = [];

  startTrace(
    workflowId: string,
    agentName: string,
    input: AgentTrace["input"],
    parentTraceId?: string
  ): string {
    const traceId = `trace-${Date.now()}-${Math.random()
      .toString(36)
      .slice(2, 8)}`;

    this.traces.push({
      traceId,
      workflowId,
      agentName,
      parentTraceId,
      startTime: Date.now(),
      input,
      metadata: {},
    });

    return traceId;
  }

  endTrace(
    traceId: string,
    output: AgentTrace["output"],
    error?: AgentTrace["error"]
  ): void {
    const trace = this.traces.find((t) => t.traceId === traceId);
    if (trace) {
      trace.endTime = Date.now();
      trace.output = output;
      trace.error = error;
    }
  }

  getWorkflowSummary(workflowId: string) {
    const workflowTraces = this.traces.filter(
      (t) => t.workflowId === workflowId
    );

    return {
      totalAgentCalls: workflowTraces.length,
      totalTokens: workflowTraces.reduce(
        (sum, t) => sum + (t.output?.tokensUsed || 0),
        0
      ),
      totalLatencyMs: Math.max(
        ...workflowTraces.map((t) => t.endTime || 0)
      ) - Math.min(...workflowTraces.map((t) => t.startTime)),
      errors: workflowTraces.filter((t) => t.error).length,
      agentBreakdown: workflowTraces.map((t) => ({
        agent: t.agentName,
        tokens: t.output?.tokensUsed || 0,
        latencyMs: (t.endTime || 0) - t.startTime,
        toolCalls: t.output?.toolCalls || 0,
      })),
    };
  }
}

Cost Management: Token Budgets Per Agent

This is where multi-agent systems get expensive fast. Without budgets, a chatty research agent can burn through $50 of API calls on a single workflow.

Token Budget Architecture

class TokenBudgetManager {
  private budgets: Map<
    string,
    { allocated: number; used: number }
  > = new Map();

  allocate(agentName: string, tokens: number): void {
    this.budgets.set(agentName, { allocated: tokens, used: 0 });
  }

  consume(agentName: string, tokens: number): void {
    const budget = this.budgets.get(agentName);
    if (!budget) throw new Error(`No budget for ${agentName}`);

    budget.used += tokens;

    if (budget.used > budget.allocated) {
      throw new TokenBudgetExceededError(
        `${agentName} exceeded token budget: ${budget.used}/${budget.allocated}`
      );
    }
  }

  getRemaining(agentName: string): number {
    const budget = this.budgets.get(agentName);
    if (!budget) return 0;
    return Math.max(0, budget.allocated - budget.used);
  }

  getSummary(): Record<string, { allocated: number; used: number; percentage: number }> {
    const summary: Record<string, { allocated: number; used: number; percentage: number }> = {};
    for (const [name, budget] of this.budgets) {
      summary[name] = {
        ...budget,
        percentage: Math.round((budget.used / budget.allocated) * 100),
      };
    }
    return summary;
  }
}

// Usage
const budgetManager = new TokenBudgetManager();
budgetManager.allocate("supervisor", 50000);
budgetManager.allocate("research-agent", 100000);
budgetManager.allocate("coding-agent", 200000);
budgetManager.allocate("review-agent", 50000);
// Total budget: 400K tokens (~$2-6 depending on model)

Cost Comparison Table

Architecture Avg Tokens Per Run Approx Cost (Claude Sonnet) Approx Cost (Claude Opus)
Single agent, simple task 5K-15K $0.02-$0.07 $0.10-$0.30
Single agent, complex task 30K-80K $0.12-$0.32 $0.60-$1.60
2-agent pipeline 40K-120K $0.16-$0.48 $0.80-$2.40
3-agent pipeline 80K-200K $0.32-$0.80 $1.60-$4.00
Hierarchical (supervisor + 3 specialists) 150K-500K $0.60-$2.00 $3.00-$10.00
Adversarial (debate + judge) 200K-600K $0.80-$2.40 $4.00-$12.00

The cost difference between single-agent and multi-agent is 10-40x. Make sure the quality improvement justifies it.

Real Patterns From Production Deployments

Here are three multi-agent architectures we have deployed in production, with honest assessments.

Case 1: Document Processing Pipeline (Sequential)

Client: Legal tech startup processing contracts.

Architecture: 4-stage sequential pipeline (extract, analyze, summarize, validate).

What worked:

  • Clear separation of concerns. Each agent had a focused job.
  • The validation agent caught 94% of hallucinations from the summary agent.
  • Total processing time: 45 seconds per contract (vs 15 minutes manual).

What did not:

  • The extraction agent occasionally missed nested clauses, which cascaded through the entire pipeline.
  • We had to add a "confidence score" to each stage to decide whether to proceed or flag for human review.
  • Cost was $0.80 per contract, which the client initially found high until they compared it to paralegal rates.

Case 2: Code Review System (Parallel Fan-Out)

Client: Internal tool for a mid-size engineering team.

Architecture: 3 parallel review agents (security, performance, style) + 1 merger agent.

What worked:

  • Parallel execution cut review time from 90 seconds (sequential) to 35 seconds.
  • Each agent was tuned with domain-specific system prompts and examples.
  • The merger agent resolved conflicts surprisingly well.

What did not:

  • The security agent was too aggressive, flagging false positives 40% of the time. We had to add a "severity threshold" and few-shot examples of acceptable patterns.
  • Token costs were 3x what we estimated. The performance agent was including full stack traces in its analysis.
  • We eventually moved style review to a deterministic linter and kept only security and performance as agent-based, reducing costs by 35%.

Case 3: Customer Support Triage (Hierarchical)

Client: E-commerce company with 50K+ support tickets per month.

Architecture: Supervisor + 4 specialists (billing, shipping, product, escalation).

What worked:

  • Correct routing accuracy: 91% (after 3 months of tuning).
  • Average resolution time for simple queries: 8 seconds.
  • Customer satisfaction scores improved by 23%.

What did not:

  • The supervisor agent initially routed too many tickets to the escalation (human) specialist. It was being overly cautious.
  • We had to add explicit routing rules (deterministic) for common patterns and only use the supervisor for ambiguous cases. This hybrid approach cut agent costs by 60%.
  • Edge cases (multi-issue tickets) still required human intervention 15% of the time.

Anti-Patterns to Avoid

We have seen these mistakes repeatedly, both in our own work and in projects we have been called in to fix.

Anti-Pattern 1: Agent Soup

Throwing 10 agents at a problem because "more agents = better."

Symptom: Token costs are astronomical, latency is measured in minutes, and the output is no better than a single well-prompted agent.

Fix: Start with one agent. Add a second only when you can prove the first cannot handle the task. Add a third only when you can prove two cannot.

Anti-Pattern 2: Infinite Delegation

Agents delegating to agents delegating to agents.

Symptom: Workflows that never terminate, exponential token consumption.

Fix: Hard limit on delegation depth (we use 3 levels max) and total token budgets that force completion.

Anti-Pattern 3: No Validation Between Stages

Blindly passing output from one agent to the next.

Symptom: Errors in early stages compound into nonsensical final output.

Fix: Structured output schemas and validation at every handoff point.

Anti-Pattern 4: Ignoring Deterministic Alternatives

Using agents for tasks that can be solved with regular code.

Symptom: An "agent" whose job is to parse JSON or format dates.

Fix: Use agents for reasoning and judgment. Use code for everything else.

The Production Checklist

Before deploying any multi-agent system, we go through this:

Item Status Check
Can a single agent do this? Tested and documented why not
Token budgets per agent Set and enforced
Total workflow budget Set with hard cutoff
Retry policies per stage Configured with backoff
Circuit breakers for external APIs Implemented
Structured output validation At every handoff
Delegation depth limit Max 3 levels
Timeout per agent call Set (default 30s)
Trace logging Every agent call traced
Cost monitoring dashboard Live and alerting
Graceful degradation Partial results + human queue
Load testing Run at 2x expected volume

Where This Is Heading

The multi-agent space is evolving rapidly. Claude's Opus 4.6 with its million-token context window and native agent-team capabilities is making some of these patterns simpler. Anthropic's internal benchmarks show agent teams on Opus 4.6 outperforming previous multi-agent setups by significant margins, primarily because the larger context window reduces the need for context splitting.

We expect that within a year, many of the orchestration patterns we described here will be abstracted into frameworks. But understanding the underlying patterns will remain essential for debugging, optimization, and cost control.

At CODERCOPS, our approach is pragmatic: we use the simplest architecture that solves the problem. Sometimes that is a single agent with good tools. Sometimes it is a carefully orchestrated team. The skill is knowing which situation calls for which approach.

Need help architecting AI agent systems for your product? CODERCOPS has shipped multi-agent deployments across legal tech, e-commerce, and developer tools. Talk to us about your use case.

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