You Can Build an Agentic AI Workflow. No Coding, No Engineering, No Problem.

How a business developer went from prompts to a validated, repeatable, no-code agentic workflow.

After years of sitting across the table from enterprise AI technical teams as a business practitioner, I decided to stop observing and start building.

There is a lot of noise right now about agentic AI.

Much of it is written by data scientists, IT professionals, and computer science engineers for technologists. The conversation is full of terms like "LLM pipelines," "workflow orchestration," "multi-agent frameworks," and "retrieval-augmented generation (RAG)” language that leaves business practitioners feeling like this belongs to someone else.

It doesn't. And I want to show you why.

I recently built an agentic AI workflow on Claude from scratch. Not as a coder or developer, but as a business developer with deep commercial acumen, a deep low-code, no-code, and AI background, and a clear use case in mind. *Note: The master prompt that runs the entire workflow is ready for you in the addendum below.

What I discovered is that the hard part of agentic AI isn't the technology. It's knowing...

• What the goal is?
• What questions to ask?
• How to frame the process?
• What a good output looks like?
• Whether to trust the answer you get.

And perhaps most importantly: be open to finding out what you don't know. The best way to learn is to stop waiting and start building.

What "Agentic" Actually Means for Business Practitioners

Strip away the jargon and agentic AI is simple: instead of you asking the AI a question and getting an answer, the agentic AI decides what questions to ask next, runs them in sequence, and surfaces findings you didn't explicitly request.

It plans. It adapts. It catches things.

Think of the difference between hiring a researcher to answer one question at a time versus handing an analyst a business objective and letting them run. They return only when they have something worth reporting.

The difference in practice:

• Traditional AI: You write a prompt, you get an answer. The thinking is yours.
• Agentic AI: You define the goal and hand over the data, a spreadsheet today or a live API feed tomorrow. The AI identifies conditions, cross-references volume, flags outliers, detects data quality issues, and ranks opportunities. You didn't ask for any of that specifically. It reasoned its way there.

That shift, from answering to reasoning, is what makes agentic AI genuinely powerful for business work.

What I Built and What I Learned

For this proof of concept, I chose a pharmaceutical use case. Kaggle provided a real dataset: 53,000+ patient drug reviews spanning nearly a decade, 2,600 drugs, and 700 medical conditions.

The business question was straightforward: where are the biggest opportunities to grow drug markets by improving patient outcomes?

I worked through each stage of the workflow deliberately.

The AI didn't just answer my questions. It autonomously detected that date fields were stored as Excel serial numbers and needed correction, identified a miscategorized drug appearing in the wrong condition, flagged that review text had been replaced with synthetic placeholders, and surfaced a 5x growth in mental health reviews that I hadn't asked about. Each of those findings came without a prompt.

Three things make this provable rather than just claimable:

1. Tasks: The agent ran tasks that I did not ask for. It decided which sub-questions were worth pursuing entirely on its own.
2. Sequence: The outputs were sequential and dependent. The AI determined the correct order of insights itself, rather than following a predefined script.
3. Prioritization: It produced a prioritized recommendation, not a data dump. Four analyses were synthesized into a single dashboard with a ranked opportunity matrix.

"The hard part wasn't building the workflow. It was knowing when to trust the output, when to request clarification, and when to push back entirely."

That's the human-in-the-loop principle in action. I validated every key finding before it went into the executive narrative. One of the AI's headline conclusions turned out to be a clinical mismatch, specifically an opioid appearing as the top-rated antidepressant. Catching that, and explaining why, is what separates an analyst from someone who just runs a tool.

Repeatability is the final test. A single master prompt, given a clean dataset, runs all nine stages and produces the same structured output every time. The workflow is the intellectual property. The dataset is just the input.

Want to try it yourself? See the addendum at the end of this post for the exact prompt and step-by-step guide.

The dashboard below, which is interactive in the HTML version, shows the workflow in action. Condition selector, year filters, satisfaction KPIs, and drug performance rankings, all live and filterable. 53,000+ rows of patient reviews turned into a live dashboard built directly from the workflow output.

What This Looks Like in Enterprise AI Deployments

This workflow didn't emerge in isolation. It draws directly on what I've seen working as a management consultant alongside global Fortune 1000 organizations on their Cloud ERP and AI adoption journeys, from prompt engineering through to complex enterprise deployments where AI is embedded across commercial, customer-facing, and operational platforms.

Across those engagements, a few things are consistently true:

• Budgets: The organizations moving the fastest are not those with the biggest technology budgets, they're the ones with business practitioners who can translate between business challenges and AI capabilities.
• Design: Prompt quality, workflow architecture, and framing drive better outcomes than model selection. How you set up the problem matters most. Claude handled every stage of this workflow without missing a beat.
• Quality: Validation is the step too many teams skip, and the one that creates the most risk in regulated industries.
• Discoveries: The most valuable AI outputs are the ones that change a decision, not the ones that confirm what was already believed.

The future of AI adoption will be led by people who understand and continuously learn about the business, not people who primarily understand the technology.

This Is Our Moment

If you're a business developer sitting on the sidelines of the AI conversation, this is the moment to step in. We don't need to learn to code. We need to apply the business expertise built around our skills, education, and experiences.

• Think in workflows, not just questions
• Ask precise, goal-driven prompts
• Know when an answer deserves scrutiny and when to push back

The agentic AI space is evolving fast. I'm keeping pace, staying curious, and always open to what comes next.

I'm continuing to build. Next steps include connecting this workflow to live data via API, expanding into multi-agent architecture, and taking the same framework into new industry verticals. The addendum below gives you everything you need to run this workflow on your own dataset.

If you're exploring how agentic AI can drive real outcomes in your organization, I'd like to hear the concepts you're working on.

Let's build something!

Drop a "like," or "comment" below or connect directly.

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ADDENDUM: HOW I BUILT IT. A PRACTICAL GUIDE FOR BUSINESS PRACTITIONERS

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This addendum is for business practitioners who want to replicate what I did.

No coding required. No technical background needed. Just a clean dataset from Kaggle, and access to Claude, and a clear business question in mind.

 ────────────────────────────────────────────────────────

WHAT YOU NEED BEFORE YOU START

1. Dataset: A dataset in .xlsx format with clearly labeled columns (drug name, condition, rating, date, and a review or comment field*).
2. Claude: Access to Claude at claude.ai — a Pro subscription ($20 USD/month) is recommended for heavy workflow use.
3. Question: A single business question written in plain English before you begin. Do not start without one. The question is the anchor for everything that follows.

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 THE NINE STAGES — WHAT EACH ONE MEANS IN PLAIN ENGLISH

1. Attach - Upload your file and confirm all columns loaded correctly and data is clean.
2. Set Goal - Name the condition, metric, and audience before touching the data.
3. Summary - Ask the AI to describe the dataset in plain English.
4. Explore - Ask business questions and let the AI surface patterns, ratings, and top drugs.
5. Visualize - Request charts, distributions, and trend lines to see the data clearly.
6. Agentic - Let the AI run autonomously, detecting themes, gaps, and opportunities unprompted.
7. Validate - Check every key finding before you trust it. Push back when something looks wrong.
8. Iterate - Filter by condition, drug, time period, or rating. Go deeper on what matters.
9. Executive Narrative - Ask the AI to write a business-ready summary with opinionated recommendations.

*Note: Because the comments were long (Column D), with mixed symbols, I shortened them randomly as follows which you can do as an option or leave them as is.

• Positive experience.
• Neutral experience.
• Negative experience.
• Mixed feedback.
• Satisfied with treatment.
• Dissatisfied with treatment.
• Side effects reported.
• No side effects reported.
• Effective medication.
• Ineffective medication.
• Need a phone call.
• Happy to provide a review.
• Ready to join a clinical trial.
• Nurse needs to follow up.
• Doctor needs to follow up.
• PA needs to follow up.
• Call in a new prescription to my pharmacy.

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THE MASTER PROMPT — ONE PROMPT THAT RUNS THE ENTIRE WORKFLOW

─────────────────────────────────────────────────────────────------

This is the single prompt that makes the workflow repeatable. Attach your clean dataset and paste this in full. Do not shorten it. The AI will work through all nine steps sequentially without further instruction from you. Two things to know before you run it:

• Your dataset must match the column schema above. If your column names differ, Claude will flag the mismatch in Step 1 and ask you to clarify before proceeding. That is intentional, not a bug.
• This prompt was built for a pharma use case. The clinical plausibility checks and physician engagement recommendations are specific to drug and condition data. If you apply it to a different industry, the framework and output structure will still work, simply update the domain-specific language in Steps 3 and 6 to reflect your context.

──────────────────────────────────────────────────────────────

ROLE

You are a market intelligence analyst specializing in patient outcome data. You will receive a dataset of patient drug reviews. Your job is to run a complete agentic analysis workflow and produce all the outputs below, in order, without waiting for additional instructions.

EXPECTED INPUT COLUMNS

• uniqueID — unique row identifier
• drugName — name of the drug reviewed
• condition — medical condition the drug was taken for
• review — patient review text (maybe synthetic — note if so)
• rating — patient satisfaction score, integer 1–10
• date — review date, formatted YYYY‑MM‑DD
• usefulCount — number of users who found the review helpful

 If any expected column is missing or named differently, flag it in Step 1 before proceeding. 

─────────────────────────────────────────────────────────────

STEP 1 — DATA QUALITY REPORT

─────────────────────────────────────────────────────────────

Assess the dataset and report:

• Total row count and column inventory
• Null or missing values per column, with % of total
• Date field format — confirm YYYY-MM-DD or flag and convert
• Rating range check — confirm all values fall between 1 and 10
• Duplicate row check — flag exact and near-duplicate records
• Review text check — flag if text appears synthetic or templated
• Any column that appears miscategorized or structurally inconsistent

Exclude all rows where condition is null from Steps 2–6.

State clearly how many rows were excluded and why.

Produce a RUN LOG entry:

• Rows analysed: [n]
• Rows excluded: [n] ([reason])
• Issues flagged: [list]
• Data reliable for quantitative analysis: [YES / PARTIAL / NO]

─────────────────────────────────────────────────────────────

STEP 2 — CONDITION LANDSCAPE

─────────────────────────────────────────────────────────────

Identify the top 15 conditions by review volume.

For each condition report:

• Total reviews
• Average rating (rounded to 1 decimal)
• % of reviews rated 10 (advocates)
• % of reviews rated 1–3 (detractors)
• Year-over-year trend: is volume growing, stable, or declining?

Flag any condition where:

• Average rating is below 6.5 AND review count exceeds 500

(label: HIGH PRIORITY — satisfaction gap at scale)

• Review volume has grown more than 3x between the first and last three years of the dataset

(label: ACCELERATING ENGAGEMENT)

─────────────────────────────────────────────────────────────

STEP 3 — COMPETITIVE SPREAD ANALYSIS

─────────────────────────────────────────────────────────────

For each of the top 15 conditions:

- Identify all drugs with 20 or more reviews

• Calculate average rating per drug
• Report the best-performing and worst-performing drug
• Calculate the spread (best minus worst)
• Flag any drug where clinical category appears inconsistent with the condition (e.g. an opioid appearing in a psychiatric condition) — label: MISCATEGORIZATION RISK

Rank conditions by spread, highest to lowest. Flag any condition with a spread of 3.0 or greater as a COMPETITIVE DISPLACEMENT OPPORTUNITY. 

─────────────────────────────────────────────────────────────

STEP 4 — VALIDATION CHECKPOINT

─────────────────────────────────────────────────────────────

Before producing final rankings, run these checks:

• Statistical reliability: flag any headline drug finding based on fewer than 30 reviews as LOW SAMPLE — directional only
• Clinical plausibility: flag any drug appearing in a condition where it would not typically be prescribed
• Outlier check: flag any condition with an average rating above 9.0 — investigate whether low review volume is inflating the score
• Confirm top findings pass both checks before including in the opportunity matrix

Produce a VALIDATION SUMMARY listing every flag raised, the decision made, and whether the finding was retained, adjusted, or excluded. 

─────────────────────────────────────────────────────────────

STEP 5 — OPPORTUNITY MATRIX

─────────────────────────────────────────────────────────────

Synthesize Steps 2, 3, and 4 into a ranked opportunity matrix.

Score and rank the top conditions using three signals: 

• Volume score — review count relative to dataset maximum
• Satisfaction gap — inverse of average rating (lower = higher priority)
• Competitive spread — size of best-vs-worst drug gap

Classify each condition as:

• TIER 1 — Immediate action (high volume + low satisfaction + large spread)
• TIER 2 — Strategic priority (strong on two of three signals)
• TIER 3 — Watch list (one strong signal, monitor for movement)

 For each Tier 1 and Tier 2 condition provide a one-sentence physician engagement recommendation.

─────────────────────────────────────────────────────────────

STEP 6 — EXECUTIVE NARRATIVE

─────────────────────────────────────────────────────────────

Write a structured executive summary using this schema: 

  HEADLINE FINDING — one paragraph, the single most important insight

  TOP 3 OPPORTUNITIES — one paragraph each:

      Condition name and tier

      Key statistics (volume, avg rating, spread)

      Physician engagement recommendation

METHODOLOGY NOTES — bullet list of all exclusions, flags, and limitations the reader must know

CLOSING STATEMENT — one paragraph, the business implication and recommended next step

Tone: written for a commercial or sales leadership audience.

Plain language. No technical jargon. No statistical notation.

Every number must trace back to a finding in Steps 1–5. 

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<< Important: Delete the “Repeatable Note” and everything after it below when running the Master Prompt >>

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REPEATABILITY NOTE

This prompt is designed to produce consistent, structured output against any dataset that matches the input column schema above. If the dataset differs, flag the delta in Step 1 and adapt.

Do not skip steps. Do not merge steps.

Produce each output section with a clear header before proceeding to the next.

─────────────────────────────────────────────────────────────────────

THE ONE VALIDATION RULE THAT MATTERS MOST

─────────────────────────────────────────────────────────────────────

 Before you trust any finding, ask yourself one question:

 "Does this make sense given what I know about the subject?"

In my workflow, the AI identified an opioid as the top-rated antidepressant. The number was real. The conclusion was wrong. A miscategorized drug had skewed the result. I caught it because I asked the question. That catch is the difference between analysis and insight.

No AI output should go into a presentation, a report, or a conversation with a stakeholder without a human asking that question first. That is not a limitation of the technology.

It is the job of the business analyst.

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FINAL NOTE

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The workflow above is repeatable across any industry with a structured dataset. Healthcare, financial services, retail, professional services, the stages are the same. Only the business question changes. 

If you build something using this framework, I would genuinely like to hear about it. Drop a comment or connect directly.

About the Author

Rich Blumberg is an award-winning writer and author of two acclaimed Amazon books, Novi’s Journey to the Magic Garden and Job Seeking Warriors – A Mentors Guide to Winning. He's the President of World Sales Solutions, LLC, (WSS) a business development and low-code no-code and AI services company. For 20+ years he's been an SAP consultant working with SAP ecosystem organizations around the globe. In his spare time, he is a Drexel University Alumni Board of Governors Emeritus volunteer, ENGin English tutor for Ukrainian citizens, and an aspiring guitar player.

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