Introduction

In Part 1: /en/blog/multi-agent-deep-dive-orchestrator-part1/, we designed the Brain (Orchestrator) agent. Now we build the workers — the cost-efficiency engine of the multi-agent system.

Workers do not decide what to do. They execute structured instructions from the Brain. This separation is the key to cost optimization: the Brain uses an expensive model (Claude Sonnet) for reasoning 1-2 times, while workers use cheap models (Haiku/DeepSeek) for execution 3-8 times per query.

This guide covers the complete implementation of three production worker agents: SQL Writer, Data Processor, and Formatter.

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Table of Contents

1. Worker Agent Architecture Principles
2. Tool Design Patterns
3. SQL Writer Agent (Haiku)
4. Data Processor Agent (Haiku/DeepSeek)
5. Formatter Agent (Haiku)
6. Input/Output Contracts
7. Cost Optimization for Workers
8. Error Handling Patterns
9. Testing Worker Agents
10. Adding New Workers
11. Production Checklist

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1. Worker Agent Architecture Principles

1.1 The Single Responsibility Rule

Each worker agent has exactly one job. This is non-negotiable:

Worker	Responsibility	NOT Responsible For
SQL Writer	Translate query spec to SQL	Deciding what to query
Data Processor	Apply transformations to data	Deciding which transformations
Formatter	Convert data to output format	Deciding which format

The Brain decides the what. Workers execute the how.

1.2 Worker Design Constraints

# Every worker agent must satisfy these constraints:
#
# 1. STATELESS: No memory of previous invocations
# 2. DETERMINISTIC: Same input -> same output (within model variance)
# 3. BOUNDED: Maximum execution time of 30 seconds
# 4. CHEAP: Use the cheapest model that achieves 95%+ accuracy
# 5. TYPED: Explicit input/output schemas
# 6. TESTABLE: Can be unit-tested without the full graph

1.3 The Worker Interface

All workers follow the same interface pattern:

from langgraph.types import Command
from typing import Literal

def worker_node(state: AgentState) -> Command[Literal["brain_synthesise"]]:
    """Standard worker interface.

    1. Extract task from state
    2. Build worker-specific prompt
    3. Invoke cheap model
    4. Parse and validate output
    5. Return results to Brain
    """
    task = extract_task(state)
    prompt = build_worker_prompt(task)

    result = worker_model.invoke(prompt)
    validated = validate_output(result, task.expected_schema)

    return Command(
        update=dict(
            worker_results=state["worker_results"] + [validated],
            model_costs=state["model_costs"] + [result.usage],
        ),
        goto="brain_synthesise",
    )

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2. Tool Design Patterns

2.1 Structured Tool Calls

Workers receive structured instructions, not free-form text:

# BAD: Free-form instruction
"Write a SQL query to get BBC One reach by age group"

# GOOD: Structured specification
dict(
    agent="sql_writer",
    task=dict(
        table="ott_viewing_events",
        filters=[
            dict(column="channel", operator="=", value="BBC One"),
        ],
        dimensions=["age_group"],
        aggregations=[
            dict(function="COUNT", column="viewer_id", alias="reach"),
        ],
        scan_budget_gb=10,
    ),
)

2.2 Tool Registration with LangGraph

from langchain_core.tools import tool

@tool
def execute_athena_query(
    table: str,
    filters: list,
    dimensions: list,
    aggregations: list,
    scan_budget_gb: int = 10,
) -> dict:
    """Execute a structured query against Athena.

    Args:
        table: Target table name
        filters: List of filter conditions
        dimensions: GROUP BY columns
        aggregations: Aggregation functions
        scan_budget_gb: Maximum data scan in GB

    Returns:
        dict with 'rows', 'columns', 'bytes_scanned', 'execution_time_ms'
    """
    sql = build_athena_sql(table, filters, dimensions, aggregations)
    validate_scan_budget(sql, scan_budget_gb)
    result = run_athena_query(sql)
    return result

@tool
def apply_rim_weighting(
    data: list,
    dimensions: list,
    target_weights: dict,
) -> dict:
    """Apply rim weighting to raw survey data.

    Args:
        data: Raw query results (list of rows)
        dimensions: Demographic dimensions to weight
        target_weights: Target distribution per dimension

    Returns:
        dict with 'weighted_data', 'convergence_report', 'confidence'
    """
    weighted = iterative_raking(data, dimensions, target_weights)
    return weighted

@tool
def format_output(
    data: list,
    format_type: str,
    title: str = "",
) -> dict:
    """Format processed data into requested output.

    Args:
        data: Processed/weighted data
        format_type: 'json' | 'markdown' | 'narrative'
        title: Optional title for the output

    Returns:
        dict with 'formatted_output', 'format_type'
    """
    formatted = apply_format(data, format_type, title)
    return formatted

2.3 Tool Composition

Complex operations chain multiple tools:

Operation	Tool Chain	Total Cost
Simple metric	sql_writer -> formatter	USD 0.007
Weighted metric	sql_writer -> weight_applier -> formatter	USD 0.012
Comparison	sql_writer (x2) -> weight_applier -> formatter	USD 0.017
Trend + anomaly	sql_writer (x3) -> weight_applier -> anomaly_detector -> formatter	USD 0.025

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3. SQL Writer Agent (Haiku)

The SQL Writer is the most frequently invoked worker. It translates structured query specifications into Athena-compatible SQL.

3.1 Agent Configuration

from langchain_aws import ChatBedrock
from langgraph.prebuilt import create_react_agent

sql_model = ChatBedrock(
    model_id="anthropic.claude-3-5-haiku-20241022-v1:0",
    region_name="us-east-1",
    model_kwargs=dict(max_tokens=2048, temperature=0),
)

SQL_WRITER_PROMPT = """You are an Athena SQL specialist.

Input: A structured query specification with:
- table: target table name
- filters: WHERE conditions (column, operator, value)
- dimensions: GROUP BY columns
- aggregations: SUM/AVG/COUNT functions
- scan_budget_gb: maximum data scan allowed

Output: A single Athena-compatible SQL query.

Rules:
- NEVER use SELECT * (always specify columns)
- ALWAYS include partition filters for cost control
- NEVER exceed the scan budget
- Use APPROX_COUNT_DISTINCT for large cardinality counts
- Return ONLY the SQL, no explanation
- Include a comment with estimated scan size

Example output:
-- Estimated scan: 2.5 GB
SELECT
    age_group,
    COUNT(DISTINCT viewer_id) AS reach
FROM ott_viewing_events
WHERE channel = 'BBC One'
    AND event_date BETWEEN DATE '2026-01-01' AND DATE '2026-03-01'
GROUP BY age_group
ORDER BY reach DESC
"""

sql_agent = create_react_agent(
    model=sql_model,
    tools=[execute_athena_query],
    name="sql_writer",
    prompt=SQL_WRITER_PROMPT,
)

3.2 Cost Control: Scan Budget Pattern

Athena charges per TB scanned. The SQL Writer must enforce scan budgets:

-- Workgroup limits max bytes scanned per query
-- Set this in Athena workgroup configuration:
-- BytesScannedCutoffPerQuery: 10737418240 (10 GB)

-- 100 GB = USD 0.50 max per query
-- Partition pruning is CRITICAL:
-- Always filter by event_date partition
-- Never scan more than 90 days without explicit approval

3.3 Performance Patterns

Pattern	Benefit	Example
Partition pruning	10-100x cost reduction	WHERE event_date BETWEEN …
Column selection	2-5x cost reduction	SELECT col1, col2 vs SELECT *
APPROX_COUNT_DISTINCT	3-5x speed improvement	For reach/unique viewer counts
LIMIT clause	Safety net for large result sets	LIMIT 10000
Pre-aggregated tables	100x cost reduction for common queries	Daily summary tables

---

4. Data Processor Agent (Haiku/DeepSeek)

The Data Processor handles transformations that require domain knowledge — particularly rim weighting for statistical representativeness.

4.1 Agent Configuration

PROCESSOR_PROMPT = """You are a data processing specialist.

Input: Raw query results and processing instructions from the orchestrator.
Output: Processed results with a confidence report.

Processing types:
1. RIM_WEIGHTING: Apply iterative raking across demographic dimensions
2. NORMALIZATION: Scale values to 0-100 or percentage format
3. AGGREGATION: Combine multiple result sets
4. ANOMALY_DETECTION: Flag values outside expected ranges

Rules:
- Report confidence level (high/medium/low)
- Flag any anomalies or missing data
- Return structured output matching the expected schema
- If rim weighting does not converge in 50 iterations, report failure
"""

processor_agent = create_react_agent(
    model=haiku_model,
    tools=[apply_rim_weighting, detect_anomalies],
    name="data_processor",
    prompt=PROCESSOR_PROMPT,
)

4.2 Rim Weighting Implementation

The weight applier is a hard dependency — every metric must pass through it before delivery:

def iterative_raking(data, dimensions, targets, max_iterations=50):
    """Apply rim weighting using iterative proportional fitting.

    Args:
        data: Raw survey data (list of respondent records)
        dimensions: Demographic variables to weight
        targets: Known population distributions
        max_iterations: Maximum raking iterations

    Returns:
        dict with weighted data, convergence status, and confidence
    """
    weights = initialize_weights(data)

    for iteration in range(max_iterations):
        for dim in dimensions:
            weights = adjust_dimension(weights, data, dim, targets[dim])

        if check_convergence(weights, targets, tolerance=0.01):
            return dict(
                weighted_data=apply_weights(data, weights),
                convergence_report=dict(
                    converged=True,
                    iterations=iteration + 1,
                    max_weight=max(weights),
                    min_weight=min(weights),
                ),
                confidence="high" if max(weights) <= 4.99 else "medium",
            )

    # Did not converge
    return dict(
        weighted_data=apply_weights(data, weights),
        convergence_report=dict(converged=False, iterations=max_iterations),
        confidence="low",
    )

4.3 Confidence Scoring

Confidence	Criteria	Action
High	Converged, max weight under 5.0, all dimensions balanced	Deliver to user
Medium	Converged but max weight over 5.0	Deliver with warning
Low	Did not converge, or missing dimension data	Flag for human review

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5. Formatter Agent (Haiku)

The Formatter converts processed data into the final output format. It is the cheapest worker — simple template-based transformations.

5.1 Agent Configuration

FORMATTER_PROMPT = """You are an output formatting specialist.

Input: Processed data and a format specification.
Output: Formatted result in the requested format.

Supported formats:
1. JSON: Structured data for dashboards (Recharts/D3 compatible)
2. MARKDOWN: Table format for reports
3. NARRATIVE: Plain-English summary sentence

Rules:
- JSON output must be valid and parseable
- Markdown tables must have proper headers and alignment
- Narrative summaries must be one paragraph, under 100 words
- Always include the data source and weighting status
- Round numbers to 2 decimal places
"""

5.2 Output Format Examples

JSON Format (for dashboards):

# Recharts-compatible JSON output
output = dict(
    chart_type="bar",
    title="BBC One Reach by Age Group (Weighted)",
    data=[
        dict(age_group="18-24", reach=1250000, weighted=True),
        dict(age_group="25-34", reach=1890000, weighted=True),
        dict(age_group="35-44", reach=2340000, weighted=True),
        dict(age_group="45-54", reach=2670000, weighted=True),
        dict(age_group="55-64", reach=2890000, weighted=True),
        dict(age_group="65+", reach=3120000, weighted=True),
    ],
    metadata=dict(
        source="ott_viewing_events",
        period="2026-01-01 to 2026-03-01",
        weighting="rim_weighted",
        confidence="high",
    ),
)

Markdown Format (for reports):

# Markdown table output
output = """
| Age Group | Reach (Weighted) | Share |
|-----------|-----------------|-------|
| 18-24     | 1,250,000       | 8.5%  |
| 25-34     | 1,890,000       | 12.8% |
| 35-44     | 2,340,000       | 15.9% |
| 45-54     | 2,670,000       | 18.1% |
| 55-64     | 2,890,000       | 19.6% |
| 65+       | 3,120,000       | 21.2% |

*Source: ott_viewing_events | Period: Jan-Mar 2026 | Rim weighted*
"""

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6. Input/Output Contracts

6.1 Contract Design

Every worker-to-worker interaction has a typed contract:

From	To	Contract
Brain	SQL Writer	QuerySpec -> SQL string
SQL Writer	Data Processor	ResultSet -> ProcessedData
Data Processor	Formatter	ProcessedData -> FormattedOutput
Any Worker	Brain	WorkerResult -> synthesis decision

6.2 Validation Pattern

from dataclasses import dataclass
from typing import Optional

@dataclass
class QuerySpec:
    table: str
    filters: list
    dimensions: list
    aggregations: list
    scan_budget_gb: int = 10

@dataclass
class WorkerResult:
    agent_name: str
    success: bool
    data: Optional[dict]
    error: Optional[str]
    confidence: float
    execution_time_ms: int
    token_usage: dict

def validate_worker_result(result: dict, expected_schema: dict) -> WorkerResult:
    """Validate worker output against expected schema."""
    # Check required fields
    for field in expected_schema.get("required", []):
        if field not in result:
            return WorkerResult(
                agent_name="unknown",
                success=False,
                data=None,
                error=f"Missing required field: [field]",
                confidence=0.0,
                execution_time_ms=0,
                token_usage=dict(),
            )

    return WorkerResult(
        agent_name=result.get("agent", "unknown"),
        success=True,
        data=result,
        error=None,
        confidence=result.get("confidence", 0.5),
        execution_time_ms=result.get("execution_time_ms", 0),
        token_usage=result.get("usage", dict()),
    )

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7. Cost Optimization for Workers

7.1 Model Selection by Task

Task Complexity	Model	Cost per 1M Input	When to Use
Simple SQL	Haiku 3.5	USD 0.80	Standard queries, under 2K tokens
Complex SQL	Haiku 3.5	USD 0.80	Multi-table joins, subqueries
Data processing	DeepSeek R1	USD 0.55	Statistical calculations
Formatting	Haiku 3.5	USD 0.80	Template-based output
Fallback (high complexity)	Sonnet 4	USD 3.00	Only if Haiku fails twice

7.2 Token Optimization Strategies

Strategy	Savings	Implementation
Minimal prompts	30-50%	Workers get only the task, not full context
Structured output	20-30%	Request specific fields, not free-form text
Prompt caching	90%	Cache worker system prompts on Bedrock
Batch processing	15-25%	Group multiple similar tasks
Response truncation	10-20%	Set max_tokens to expected output size

7.3 Cost per Query Breakdown

Component	Model	Tokens	Cost
Brain Plan	Sonnet 4	~10K input, 500 output	USD 0.0375
SQL Writer	Haiku 3.5	~2K input, 500 output	USD 0.0036
Weight Applier	Haiku 3.5	~3K input, 1K output	USD 0.0064
Formatter	Haiku 3.5	~2K input, 500 output	USD 0.0036
Brain Synthesise	Sonnet 4	~5K input, 500 output	USD 0.0225
Total		~22.5K tokens	USD 0.074

With prompt caching on Brain calls: USD 0.025 per query (66% reduction).

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8. Error Handling Patterns

8.1 Worker Error Types

Error	Cause	Recovery
Model timeout	Complex query, model overloaded	Retry with shorter prompt
Invalid SQL	Ambiguous query spec	Return error to Brain for re-planning
Scan budget exceeded	Query too broad	Add partition filters, reduce date range
Convergence failure	Insufficient data for weighting	Return unweighted with warning
Format error	Invalid data structure	Retry with explicit schema

8.2 Worker Retry Strategy

from langgraph.types import RetryPolicy

worker_retry = RetryPolicy(
    max_attempts=2,          # Max 2 retries (3 total attempts)
    initial_interval=0.5,    # 500ms initial wait
    backoff_factor=2.0,      # Exponential backoff
    max_interval=5.0,        # Max 5 second wait
)

# If all retries fail, escalate to Brain
def worker_with_fallback(state: AgentState) -> Command:
    try:
        result = execute_worker(state)
        return Command(
            update=dict(worker_results=state["worker_results"] + [result]),
            goto="brain_synthesise",
        )
    except Exception as e:
        return Command(
            update=dict(
                worker_results=state["worker_results"] + [
                    dict(
                        success=False,
                        error=str(e),
                        agent="sql_writer",
                    )
                ]
            ),
            goto="brain_synthesise",  # Let Brain decide how to handle
        )

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9. Testing Worker Agents

9.1 Unit Testing

def test_sql_writer_generates_valid_sql():
    """Test SQL generation with known query spec."""
    spec = dict(
        table="ott_viewing_events",
        filters=[dict(column="channel", operator="=", value="BBC One")],
        dimensions=["age_group"],
        aggregations=[dict(function="COUNT", column="viewer_id", alias="reach")],
    )

    result = sql_writer_node(mock_state(spec))
    sql = result.update["worker_results"][-1]["sql"]

    assert "ott_viewing_events" in sql
    assert "age_group" in sql
    assert "COUNT" in sql
    assert "SELECT *" not in sql  # Never SELECT *


def test_weight_applier_converges():
    """Test rim weighting with known data."""
    data = generate_test_survey_data(n=1000)
    targets = dict(
        age_group=dict(young=0.3, middle=0.4, senior=0.3),
        gender=dict(male=0.49, female=0.51),
    )

    result = iterative_raking(data, ["age_group", "gender"], targets)
    assert result["convergence_report"]["converged"] is True
    assert result["confidence"] in ["high", "medium"]

9.2 Integration Testing

Test	Input	Expected
SQL -> Athena roundtrip	Simple metric query	Valid result set with correct columns
Weight applier accuracy	Known population data	Weighted distribution within 1% of targets
Formatter JSON output	Processed data	Valid JSON parseable by Recharts
Full pipeline	Natural language question	Correct weighted answer

9.3 LangSmith Evaluation Datasets

Create evaluation datasets for each worker:

Dataset	Size	Purpose
sql_accuracy	100 query specs	SQL correctness, scan efficiency
weighting_accuracy	50 datasets	Convergence rate, weight quality
format_quality	30 format requests	Output validity, readability

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10. Adding New Workers

The system is designed for extensibility. Adding a new worker requires:

10.1 Steps to Add a New Worker

1. Define the tool with typed inputs/outputs
2. Write the worker prompt (keep it under 500 tokens)
3. Create the worker node following the standard interface
4. Register with the Brain by updating the Brain prompt
5. Add to the graph with edges to/from brain_synthesise
6. Write tests for the new worker
7. Create LangSmith evaluation dataset (minimum 20 examples)

10.2 Example: Adding an Anomaly Detector

@tool
def detect_anomalies(
    data: list,
    metric: str,
    threshold_sigma: float = 2.0,
) -> dict:
    """Detect statistical anomalies in time series data.

    Args:
        data: Time series data (list of records with date + value)
        metric: Name of the metric column to analyse
        threshold_sigma: Number of standard deviations for anomaly threshold

    Returns:
        dict with 'anomalies', 'baseline', 'analysis'
    """
    values = [row[metric] for row in data]
    mean = sum(values) / len(values)
    std = calculate_std(values)

    anomalies = []
    for row in data:
        z_score = abs(row[metric] - mean) / std
        if z_score > threshold_sigma:
            anomalies.append(dict(
                date=row["date"],
                value=row[metric],
                z_score=round(z_score, 2),
                direction="above" if row[metric] > mean else "below",
            ))

    return dict(
        anomalies=anomalies,
        baseline=dict(mean=round(mean, 2), std=round(std, 2)),
        analysis=f"Found anomalies out of data points",
    )

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11. Production Checklist

Worker Design

• Each worker has exactly one responsibility
• All workers use the standard interface (Command return)
• Input/output contracts are typed and validated
• Workers are stateless and deterministic

Cost Control

• Workers use Haiku/DeepSeek (not Sonnet)
• Prompts are minimal (under 500 tokens for workers)
• Prompt caching enabled for worker system prompts
• Scan budgets enforced for SQL queries

Error Handling

• Retry policy configured (max 2 retries)
• Fallback to Brain on worker failure
• Graceful degradation (partial results over no results)
• Error context logged for debugging

Testing

• Unit tests for each worker function
• Integration tests for tool chains
• LangSmith evaluation datasets (minimum 20 per worker)
• Regression tests in CI pipeline

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Next in the Series

• Part 1: Orchestrator Agent Deep Dive: /en/blog/multi-agent-deep-dive-orchestrator-part1/ — Brain Architecture, Routing Logic, Prompt Engineering
• Part 3: State Management and Data Layer — DynamoDB checkpointing, S3 state, Athena integration
• Part 4: Security Architecture — IAM, prompt injection defense, audit logging
• Part 5: CI/CD Pipeline for Agents — LangSmith eval, prompt versioning, canary deploys
• Part 6: Scaling Patterns and Production — Step Functions, auto-scaling, cost optimization

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