AI Agents for Evaluators:
Build No-Code AI Agents for M&E Workflows

Learn how to design, build, test, and use practical AI agents that support evaluation design, reporting, qualitative coding, quantitative analysis, data quality review, indicator tracking, donor updates, learning, and follow-up.

This course is for professionals who want AI to reduce repetitive work, improve consistency, and support better evidence use — while keeping human judgment, validation, and ethics at the center.

Reports, coding tables, donor updates, trackers, and learning notes often take hours to prepare manually.

AI can produce fluent text, but M&E work needs evidence, traceability, limitations, and careful judgment.

The real value is not just prompts. It is connecting inputs, tools, outputs, validation checks, and human review.

Build AI agents that support evaluation work — without replacing the evaluator.

You will learn how to design AI agents that work with structured inputs, approved source materials, validation checklists, and human quality gates. The course focuses on responsible automation, not blind AI use.

• Design AI agents for common M&E workflows.
• Build reusable prompts with validation rules.
• Prepare safe input packs and source folders.
• Connect tools such as ChatGPT, Claude, Sheets, Excel, Airtable, Make.com, and Zapier.
• Validate AI outputs against evidence.
• Create a final AI agent package for your own evaluation practice.

Build agents for evaluation matrices, synthesis, reporting, recommendations, and follow-up.

Use AI to support routine monitoring, data quality review, indicator tracking, and donor updates.

Turn evidence into clearer learning, action plans, and management follow-up.

Learn practical, responsible AI workflows for qualitative and quantitative evaluation tasks.

Foundations of AI Agents for Evaluators

Understand what AI agents are, how they differ from simple prompts, and how to build your first reusable evaluation agent.

Design and Specification

Map workflows, assess risks, define inputs and outputs, and create agent specifications before building.

Implementation and Deployment

Build agents with ChatGPT, Claude, documents, spreadsheets, and workflow automation platforms.

Verification and Reliability

Check outputs against source evidence, build validation checklists, and create quality gates for AI-assisted work.

Practical AI Agents for M&E Workflows

Apply all previous modules to build seven practical agents for evaluation design, reporting, qualitative coding, quantitative analysis, data quality, donor updates, and learning follow-up.

Build Your Own AI Agent

Design, build, test, and document one practical AI agent for your own evaluation workflow.

Evaluation Design Agent

Frame evaluation questions, criteria, methods, and evaluation matrix rows.

Reporting Agent

Draft clear, evidence-based reports, summaries, briefs, and donor updates.

Qualitative Coding Agent

Support interview, focus group, and open-ended survey coding with human review.

Quantitative Analysis Agent

Summarize survey and monitoring data, trends, disaggregation, and interpretation notes.

Data Quality Agent

Review missing data, duplicates, outliers, inconsistent categories, and issue logs.

Indicator Tracking & Donor Update Agent

Turn target-versus-actual data into transparent donor updates and progress summaries.

Learning and Follow-Up Agent

Convert validated findings into recommendations, action plans, and follow-up trackers.

Two certificates · One purchase · Lifetime access

Designed around evaluation standards, evidence, and accountability.

This course is not about using AI to write faster text. It is about building responsible systems that help M&E professionals work better while protecting evidence quality, ethics, and human judgment.

Leave with your own AI agent package.

At the end of the course, you will build one AI agent for your own M&E workflow. You will define its purpose, prepare inputs, write the prompt, map tool connections, test outputs, and create validation notes.

• Agent name and purpose
• Workflow problem statement
• Tool connection map
• Final agent prompt
• Sample input and output
• Validation checklist
• Human review plan

Do I need to be technical?

No. The course starts with practical, no-code workflows. You can begin with ChatGPT, Claude, Gemini, Copilot, Excel, Google Sheets, or Word. Automation tools such as Make.com and Zapier are introduced step by step.

Is this course only about prompt engineering?

No. Prompting is included, but the course goes further. You will learn how to design inputs, connect tools, structure outputs, validate evidence, and keep human review in the workflow.

Can I use this in donor-funded or NGO work?

Yes. The course is designed for M&E, MEAL, evaluation, development, and humanitarian contexts. It emphasizes evidence quality, privacy, limitations, transparency, and human oversight.

Will AI replace the evaluator?

No. The course teaches AI as a support system. The evaluator remains responsible for judgment, interpretation, ethics, recommendations, and final decisions.

Should I take this course or the bundle?

Choose this course if your main goal is to build AI agents for evaluation workflows. Choose the bundle if you also want the broader foundation of responsible AI use across evaluation design, analysis, reporting, ethics, and human oversight.