AI Agents in Production: MCP to Market Playbook

Inspiration

AI agents are the hot topic right now but almost nobody is talking about what happens after the prototype. How do you actually test an agent? How do you deploy it safely? What does responsible AI look like when your agent is making real decisions?

I wanted to build a presentation that answers those questions for business leaders and investors. Not a hype deck full of buzzwords, but a real playbook covering everything from MCP architecture to production deployment. When I saw this hackathon, I thought it would be a great test: can I use nothing but prompt engineering with the PresentMe API Agent to generate something an investor would actually take seriously?

What it does

A 10-slide investor presentation covering the full AI agent lifecycle:

• What AI agents are and how they differ from chatbots and RPA
• Anthropic's Model Context Protocol as the standard for connecting agents to enterprise tools
• Real-world use cases across customer support, data analysis, and financial reporting
• A responsible AI framework covering fairness, transparency, and accountability
• Testing strategies built specifically for AI agents
• Production deployment best practices
• ROI benchmarks with cost reduction data
• A 90-day implementation roadmap

The audience is investors and C-suite executives. The tone is professional and data-driven. Every slide was described individually in the prompt to give the API maximum clarity on what to generate.

How I built it

I built everything by talking to the PresentMe API Agent on the documentation page. I didn't write a single line of code myself. The agent generated all of it.

I structured my interaction across 4 rounds, each using different prompting techniques:

Round 1 - Exploratory Prompting: I started by asking the agent what parameters the API supports, which creation mode works best for investor decks, how detailed the topic field can be, and how to retrieve the presentation after generation. This was important because the answers shaped everything I asked for in the next rounds. I learned that thematic mode is best for professional decks, that the topic can be very detailed, and that the API works asynchronously.

Round 2 - The Main Request (Persona + Mega-Prompting + Chain of Thought + Constraints): I asked the agent to write a complete Python script. I framed it as an expert API integration task, gave a detailed topic that described each of the 10 slides individually, specified thematic mode with an investor audience, and walked through the script structure step by step: setup, POST request, async polling, retrieval, and output. I set constraints on error handling, API key management, polling format, and timeout behavior.

Round 3 - Iterative Refinement with Few-Shot Examples: I came back with specific improvements. I gave the agent exact examples of what the success output and timeout output should look like. I also asked it to handle the 402 insufficient credits error specifically, since I learned from testing that credits get reserved at task start.

Round 4 - Self-Critique and Verification: I asked the agent to review its own code one final time for bugs, verify all endpoint URLs, confirm the API key was set to a placeholder, and make sure the script exits cleanly on timeout without making invalid API calls. Then I got the complete final version.

I also had casual back-and-forth with the agent throughout, asking about capabilities, troubleshooting issues, and understanding how the async flow works. The conversation was real and iterative, not a single copy-paste job.

Prompting techniques used: Exploratory Prompting, Persona Prompting, Mega-Prompting, Chain of Thought, Constraint-Based Prompting, Few-Shot Prompting, Iterative Refinement, Self-Critique and Verification.

Challenges I ran into

Getting the topic format right. My first attempts used a long paragraph-style topic description. The API accepted these requests but generation took a very long time and sometimes didn't complete within the polling window. After finding the platform's pro-tip about being specific with slide descriptions, I switched to a shorter format where each slide is described individually. That made a huge difference in reliability.

Understanding the async flow. The PresentMe API is fully asynchronous, so you submit a request, get a task ID, and then poll for status. Complex presentations can take a while to generate. I had to learn what realistic wait times look like and build my polling logic accordingly. The agent was really helpful here, walking me through the exact endpoints and explaining how the status transitions work.

Credit management. Credits are reserved when the API accepts your task, not when it finishes. That means even tasks that take a long time or time out from the client side still consume credits. I learned to be strategic: test with simpler requests first, understand the cost per task, and avoid retrying blindly.

Browser and session differences. I noticed some variation in agent responses across different sessions and browsers. Some of this was probably server load, some might have been account-level differences. It was a good reminder that real-world API behavior isn't always perfectly consistent and you need to build for that.

Accomplishments that I'm proud of

Generated a complex, multi-topic investor presentation entirely through conversation with the API Agent. No manual code. Every line came from the chatbot.

Used 8 distinct prompting techniques across 4 structured rounds plus natural troubleshooting conversations. The interaction wasn't scripted to look clean. It was a real iterative process where I explored, built, hit problems, adapted, and got to a result I'm happy with.

Figured out the async timing, credit system, and optimal topic format through hands-on experimentation. That kind of practical knowledge doesn't come from reading docs. It comes from actually building.

The final presentation covers a genuinely useful topic. AI agents in production is something companies are actively trying to figure out right now, and this deck could be used in a real boardroom conversation.

What I learned

Ask questions before you build. The exploratory round changed everything. Understanding the API parameters, creation modes, and async behavior upfront saved me from a lot of wasted effort.

Specificity beats length. A concise topic with clear per-slide descriptions worked better than a long, detailed paragraph. The API responded much more reliably to structured, focused input.

Iteration is where quality happens. Round 1 gets you maybe 70% of the way there. The refinement and self-review rounds are what close the gap. Giving the agent concrete examples of what you want (few-shot prompting) produces noticeably better output than vague improvement requests.

Treat prompt engineering like a real skill. This wasn't just about asking nicely. It was about choosing the right technique for the right moment: exploration when you need information, constraints when you need precision, examples when you need consistency, self-review when you need reliability.

What's next for AI Agents in Production: MCP to Market Playbook

This workflow could be a repeatable template for any business presentation. Quarterly reviews, board decks, sales enablement, architecture briefs. Describe what you want slide by slide, let the API generate it, review and iterate.

I'd also like to explore using context_files to upload whitepapers or research as source material. That should make the content even more accurate and domain-specific.

Longer term, combining the PresentMe API with live data sources (pulling numbers from analytics platforms, feeding them into presentation generation) could create a fully automated reporting pipeline. That's where this approach gets really powerful.

Built With

• agents
• ai
• presentmeapi
• presentmeplatform
• python