What You’ll Build
A practical AI agent that automates your daily workflows—from scheduling and task management to report generation and data analysis. This tutorial will show you how to build a no-code/low-code AI assistant tailored to your specific evaluation and M&E needs.

🔧 What You’ll Need:

• Google AntiGravity or any cloud development environment
• Google Gemini Pro or OpenAI GPT (primary LLM)
• Python (basic knowledge helpful but not required)
• API Access to Google Calendar, Microsoft To Do, Notion, or similar tools
• 2-3 Hours of focused building time

✅ Key Insight: You’re not coding—you’re managing an AI agent. The most valuable skill is defining outcomes, not writing functions.

Take It Further: Ready to build professional AI agents for evaluation? The AI Agents for Evaluators Certificate provides structured learning, practical workflows, and certification.

⚠️ The Problem:

• Reports, coding tables, donor updates, and trackers take hours to prepare manually
• AI can produce fluent text, but M&E work needs evidence, traceability, and careful judgment
• The real value is connecting inputs, tools, outputs, validation checks, and human review

✅ The Solution: Build AI agents that support evaluation work — without replacing the evaluator.

Think of your AI agent as a chronicler:

A system that chronicles, organizes, and improves your evaluation workflows.

import os
import requests
from datetime import datetime, timedelta, date
from dotenv import load_dotenv
load_dotenv()
def fetch_evaluation_data(data_type, target_date=None):
    """
    Fetches evaluation-related data from configured sources.
    
    For M&E workflows, this might fetch:
    - Indicator tracking data
    - Survey results
    - Program monitoring data
    - Donor report deadlines
    - Evaluation milestones
    """
    target_date = target_date or date.today()
    
    # Configure your data sources
    DATA_SOURCES = {
        'indicators': os.getenv('INDICATOR_API_URL'),
        'surveys': os.getenv('SURVEY_API_URL'),
        'monitoring': os.getenv('MONITORING_API_URL')
    }
    
    all_data = []
    
    for source_name, api_url in DATA_SOURCES.items():
        if not api_url:
            continue
            
        try:
            response = requests.get(f"{api_url}?date={target_date}", timeout=30)
            response.raise_for_status()
            all_data.extend(response.json())
        except Exception as e:
            print(f"Error fetching {source_name}: {e}")
            continue
    
    return all_data

def process_evaluation_data(raw_data, analysis_type):
    """
    Processes evaluation data based on analysis type.
    
    For M&E workflows, this might:
    - Calculate indicator performance
    - Identify data quality issues
    - Generate trend analysis
    - Create summary tables for reporting
    """
    
    processed_results = {
        'summary': [],
        'trends': [],
        'alerts': [],
        'recommendations': []
    }
    
    # Example: Process indicators
    if analysis_type == 'indicators':
        for indicator in raw_data:
            # Calculate achievement against targets
            achievement = (indicator['actual'] / indicator['target']) * 100
            
            # Flag issues
            if achievement < 80:
                processed_results['alerts'].append({
                    'indicator': indicator['name'],
                    'status': 'Below Target',
                    'achievement': f"{achievement:.0f}%"
                })
            else:
                processed_results['summary'].append({
                    'indicator': indicator['name'],
                    'achievement': f"{achievement:.0f}%",
                    'status': 'On Track'
                })
    
    return processed_results

template = """
You are an experienced M&E professional and evaluation coordinator.
Your task is to plan and organize evaluation activities based on current data and priorities.
**TODAY'S DATE:** {date}
**EVALUATION MILESTONES:** {milestones}
**INDICATOR DATA:** {indicators}
**PRIORITIES:** {priorities}
**RECENT FINDINGS:** {findings}
**EVALUATION RULES:**
1. Prioritize data quality review before analysis
2. Include triangulation steps for key findings
3. Maintain traceability to source data
4. Flag any data quality issues immediately
5. Save draft reports to {report_location}
**YOUR JOB:**
1. Create a daily evaluation plan for {date}
2. Prioritize analysis based on data quality and urgency
3. Generate initial findings for review
4. Prepare report structure
5. Identify gaps in evidence
**FORMAT:**
Start with a brief overview of the day's evaluation priorities, then provide:
09:30-11:00 | Data Quality Review - [Source]
11:00-12:30 | Indicator Analysis - [Dataset]
12:30-13:30 | Documentation & Synthesis
...
text
After the schedule, ask about any additional data sources or clarification needs.
"""

import openai
from datetime import date
def run_evaluation_agent(environment="staging"):
    """
    Main orchestration function for the evaluation agent.
    """
    # 1. Gather data from all sources
    today = date.today()
    milestones = get_evaluation_milestones(today)
    indicators = fetch_indicator_data(today)
    priorities = get_priority_rules()
    findings = get_recent_findings(environment)
    
    # 2. Build the prompt
    prompt = template.format(
        date=today,
        milestones=format_milestones(milestones),
        indicators=format_indicators(indicators),
        priorities=priorities,
        findings=format_findings(findings),
        report_location=f"reports/{today}_eval_report.md"
    )
    
    # 3. Generate plan with LLM
    client = openai.OpenAI()  # or Gemini, Claude
    response = client.chat.completions.create(
        model="gpt-4",  # or gemini-2.5-flash-lite
        messages=[
            {"role": "system", "content": "You are an expert M&E professional."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.3,
        max_tokens=4096
    )
    
    # 4. Parse and execute the plan
    evaluation_plan = parse_plan(response.choices[0].message.content)
    return execute_plan(evaluation_plan)

def generate_donor_update(indicator_data, donor_preferences):
    """
    Creates transparent donor updates from indicator data.
    """
    prompt = f"""
    Create a donor update from this indicator data:
    {indicator_data}
    
    Requirements:
    - Show actual vs target for each indicator
    - Highlight achievements and challenges
    - Maintain transparency about data limitations
    - Include next steps
    - Tailor to {donor_preferences['format']}
    
    Generate a clear, evidence-based update.
    """
    
    return llm.generate(prompt)

def qualitative_coding_agent(transcripts, codebook):
    """
    Supports interview and focus group coding with human review.
    """
    
    coding_prompt = f"""
    Code these interview transcripts:
    {transcripts}
    
    Using this codebook:
    {codebook}
    
    For each code, provide:
    1. Example quotes
    2. Frequency count
    3. Emerging themes
    4. Potential bias flags
    
    Include confidence levels and require human verification for ambiguous cases.
    """
    
    return llm.generate(coding_prompt)

def data_quality_agent(dataset, quality_rules):
    """
    Reviews data quality for missing data, duplicates, outliers, and inconsistencies.
    """
    
    quality_prompt = f"""
    Review this dataset for quality issues:
    {dataset}
    
    Using these quality rules:
    {quality_rules}
    
    Check for:
    1. Missing data patterns
    2. Duplicate entries
    3. Outliers and anomalies
    4. Inconsistent categories
    5. Validity issues
    
    Generate an actionable quality report with priority flags.
    """
    
    return llm.generate(quality_prompt)

Key Insight:

You’re managing an AI assistant, not just coding functions.

❌ Avoid: “Write a function that fetches data and calculates indicators”

✅ Do: “I need a weekly indicator summary with trends and alerts, ready for the team meeting at 10am”

# Instead of:
"Generate evaluation report."
# Provide context:
"""
Context: This is for the Q2 donor report.
Focus: Education program outcomes.
Priority: Show impact over baseline.
Data sources: School enrollment data, test scores, and teacher training records.
Format: Executive summary with key findings, graphs, and recommendations.
"""

# DO THIS:
API_KEY = os.getenv('OPENAI_API_KEY')  # Use environment variables
load_dotenv()                          # Load from .env file
sanitize_output(response)              # Clean outputs
# NOT THIS:
API_KEY = 'sk-...'                     # Never hardcode keys
hardcoded_values                        # No hardcoded credentials
directly_output_llm_response           # Always validate outputs

# Your first version - just 50 lines
def simple_eval_agent():
    """Basic evaluation agent to get started."""
    # 1. Get today's evaluation priorities
    # 2. Run the analysis
    # 3. Report findings
    pass

Ready to Build Professional AI Agents for Evaluation?

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The revolution isn’t in the technology – it’s in what we can now build as individuals.

Your evaluation assistant starts today. Build it, test it, share it.

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Get certified in building AI agents for evaluation with the AI Agents for Evaluators Certificate from EvalCommunity Academy.

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