Cost-Efficient Multi-Agent Coding Workflows

Written by Michael Lip · Solo founder of Zovo · $400K+ on Upwork · 100% JSS Join 50+ builders · More at zovo.one

A 5-agent coding workflow optimized for cost runs at $9.00 per sprint instead of $25.00. The three techniques: model routing (Opus for reasoning, Haiku for execution), prompt caching (90% savings on shared context), and iteration budgets (cap runaway loops). Combined, they reduce fleet operating cost by 64% without sacrificing output quality.

The Setup

You have a multi-agent system that handles end-to-end feature development: one agent writes code, one writes tests, one handles documentation, one reviews for quality, and one manages the CI/CD pipeline integration. All five currently run Opus 4.7 at $5.00/$25.00 per million tokens.

After analyzing the workflow, you discover that the test writer and documentation agent follow strict templates – ideal for Haiku at $1.00/$5.00. The code writer and reviewer need complex reasoning – Opus stays. The CI/CD agent runs structured commands – Haiku.

Real-world production fleet data: this architecture has produced 2,816 articles at $0.36 per article amortized, running 5 agents in parallel during 30-60 minute sprint sessions at $1,000/month total.

The Math

Before optimization (all Opus 4.7):

After optimization (2 Opus + 3 Haiku):

Agent Role Model Input Cost Output Cost Total
1 Code writer Opus 4.7 $2.50 $2.50 $5.00
2 Test writer Haiku 4.5 $0.50 $0.50 $1.00
3 Documentation Haiku 4.5 $0.50 $0.50 $1.00
4 Code reviewer Opus 4.7 $2.50 $2.50 $5.00
5 CI/CD manager Haiku 4.5 $0.50 $0.50 $1.00

Optimized total: $13.00/sprint (48% savings)

With prompt caching on shared codebase context (50K tokens):

Fully optimized: $12.26/sprint (51% savings)

Production fleet equivalent (subscription model):

The Technique

Here is a complete cost-optimized multi-agent coding workflow:

import anthropic
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass

client = anthropic.Anthropic()

OPUS = "claude-opus-4-7-20250415"
HAIKU = "claude-haiku-4-5-20251001"


@dataclass
class WorkflowConfig:
    """Configuration for a cost-optimized coding workflow."""
    code_writer_model: str = OPUS     # Complex reasoning required
    test_writer_model: str = HAIKU    # Template-based
    doc_writer_model: str = HAIKU     # Structured output
    reviewer_model: str = OPUS        # Complex reasoning required
    cicd_model: str = HAIKU           # Command execution


class CodingWorkflow:
    def __init__(self, config: WorkflowConfig = None):
        self.config = config or WorkflowConfig()
        self.cost_log = []

    def _call(self, model: str, role: str, system: str,
              message: str, max_tokens: int = 4096) -> str:
        response = client.messages.create(
            model=model,
            max_tokens=max_tokens,
            system=[{
                "type": "text",
                "text": system,
                "cache_control": {"type": "ephemeral"}
            }],
            messages=[{"role": "user", "content": message}]
        )

        usage = response.usage
        prices = {"input": 5.0 if "opus" in model else 1.0,
                  "output": 25.0 if "opus" in model else 5.0}
        cost = (usage.input_tokens * prices["input"] +
                usage.output_tokens * prices["output"]) / 1e6

        self.cost_log.append({
            "role": role, "model": model.split("-")[1],
            "tokens_in": usage.input_tokens,
            "tokens_out": usage.output_tokens,
            "cost": cost
        })
        return response.content[0].text

    def write_code(self, spec: str, codebase: str) -> str:
        return self._call(
            self.config.code_writer_model, "code_writer",
            "You are a senior developer. Write production code.",
            f"Specification:\n{spec}\n\nExisting code:\n{codebase}"
        )

    def write_tests(self, code: str, spec: str) -> str:
        return self._call(
            self.config.test_writer_model, "test_writer",
            "Write unit tests. Follow pytest conventions. "
            "Cover edge cases.",
            f"Code to test:\n{code}\n\nSpec:\n{spec}",
            max_tokens=2048
        )

    def write_docs(self, code: str, spec: str) -> str:
        return self._call(
            self.config.doc_writer_model, "doc_writer",
            "Write API documentation in markdown. Include "
            "parameters, return values, and examples.",
            f"Code:\n{code}\n\nSpec:\n{spec}",
            max_tokens=2048
        )

    def review_code(self, code: str, tests: str) -> str:
        return self._call(
            self.config.reviewer_model, "reviewer",
            "Review code for correctness, security, and "
            "performance. Be specific about issues.",
            f"Code:\n{code}\n\nTests:\n{tests}"
        )

    def setup_cicd(self, code: str, config: str) -> str:
        return self._call(
            self.config.cicd_model, "cicd",
            "Generate CI/CD configuration. Output YAML only.",
            f"Code:\n{code}\n\nExisting config:\n{config}",
            max_tokens=1024
        )

    def run(self, spec: str, codebase: str) -> dict:
        """Execute the full workflow."""

        # Phase 1: Write code (Opus - needs reasoning)
        code = self.write_code(spec, codebase)

        # Phase 2: Parallel - tests, docs, CI/CD (Haiku - structured)
        with ThreadPoolExecutor(max_workers=3) as executor:
            test_future = executor.submit(self.write_tests, code, spec)
            doc_future = executor.submit(self.write_docs, code, spec)
            cicd_future = executor.submit(self.setup_cicd, code, "")

            tests = test_future.result()
            docs = doc_future.result()
            cicd = cicd_future.result()

        # Phase 3: Review (Opus - needs reasoning)
        review = self.review_code(code, tests)

        total_cost = sum(e["cost"] for e in self.cost_log)
        return {
            "code": code, "tests": tests, "docs": docs,
            "cicd": cicd, "review": review,
            "total_cost": f"${total_cost:.4f}",
            "cost_breakdown": self.cost_log
        }


# Run workflow
workflow = CodingWorkflow()
result = workflow.run(
    spec="Add user authentication endpoint with JWT",
    codebase=open("src/main.py").read()
)

print(f"Total cost: {result['total_cost']}")
for entry in result['cost_breakdown']:
    print(f"  {entry['role']}: ${entry['cost']:.4f} ({entry['model']})")

The Tradeoffs

Cost-optimized workflows trade simplicity for savings:

Implementation Checklist

  1. Map your coding workflow into discrete agent roles
  2. Classify each role as reasoning-heavy (Opus) or execution-heavy (Haiku)
  3. Implement the multi-model workflow with per-agent tracking
  4. Add prompt caching on shared context (codebase, specifications)
  5. Set iteration budgets per agent to prevent runaway loops
  6. Run 10 test sprints and compare output quality against all-Opus baseline
  7. Calculate actual savings and validate against projected savings

Measuring Impact

Track workflow economics: