u/Outside-Risk-8912

Hey everyone,

The Model Context Protocol (MCP) is amazing for standardizing how agents talk to data, but I got incredibly frustrated every time I wanted to quickly test a new remote MCP server. Writing custom client-side boilerplate or wrestling with CLI tools just to see if a tool actually exposes the right schema is a massive time sink.

So, I built a native MCP client directly into the visual canvas of AgentSwarms.

You can now test any remote MCP server entirely in the browser without writing a single line of code.

Here is the workflow I just tested with Cloudflare: Cloudflare released a free MCP server for their documentation. Instead of building a local client to test it:

1. I dropped their SSE URL into the new MCP Servers integration in AgentSwarms.
2. The canvas immediately connected and extracted the available tools (e.g., cloudflare-docs-search).
3. I wired that tool up to a basic agent and started asking complex infrastructure questions in natural language. The agent successfully used the MCP tool to pull live docs and synthesize an answer.

Why this is useful for AI devs: If you are building your own MCP servers, you need a fast way to visually test if your endpoints are exposing tools correctly and if an LLM can actually route to them properly. This gives you an instant, visual debugging playground.

It handles the SSE connection, tool extraction, and LLM routing automatically.

It’s completely free to play with in the browser. I'd love for anyone building MCP servers right now to plug their endpoints in and see how it works.

Link: https://agentswarms.fyi

Hey Everyone,

The AI engineering job market has shifted massively in the last 6 months. Interviewers are no longer just asking "how does a transformer work?" or "how do you write a good prompt?"

They want to know if you can architect production-grade multi-agent systems, prevent RAG hallucinations, and manage state across LLM calls.

I’ve been building a visual learning sandbox for multi-agent workflows (agentswarms.fyi), and today I just launched a completely free AI Interview Prep Module inside it.

I compiled 42 top interview questions specifically for GenAI and Agentic AI roles. But instead of just giving a generic answer, the module breaks down the "Standout Answer" and teaches you the mental model of how to answer it like a senior architect.

Here are two examples from the list:

Question 1: When would you use a Multi-Agent Swarm instead of a single LLM with multiple tools?

• ❌ The average answer: "When the task is too complex, multiple agents are better than one."
• ✅ The standout answer: "You use a swarm to prevent context dilution and enforce the Principle of Least Privilege. If you give one 'God Agent' 15 tools and a 4k-word system prompt, its reliability drops and hallucination risk spikes. By routing to specialized sub-agents with narrow instructions (e.g., separating the 'Data Extraction Agent' from the 'Customer Chat Agent'), you isolate failure points and allow for parallel execution."

Question 2: How do you handle hallucinations in a financial RAG pipeline?

• ❌ The average answer: "I would lower the temperature to 0 and give it a better system prompt."
• ✅ The standout answer: "I would decouple data extraction from text generation. I'd use a deterministic node or a strict JSON-enforced agent to only extract the hard numbers from the retrieved context. Then, I would pass that structured data to a separate Synthesis Agent. Finally, I'd implement an 'LLM-as-a-judge' evaluation loop before returning the final output to the user."

What's in the full list? The 42 questions cover:

• RAG Architecture & Vector Databases
• Agentic Routing (ReAct vs. Planner-Executor)
• Evaluation metrics for non-deterministic outputs
• Security (Prompt injection prevention in multi-agent loops)

You can read through all 42 questions, answers, and the "how to answer" breakdowns right in the dashboard here: https://agentswarms.fyi/interview-questions

For those of you who have interviewed for AI Engineering roles recently, what is the hardest system design question you've been asked? I'd love to add it to the list.

Hey Everyone,

The AI engineering job market has shifted massively in the last 6 months. Interviewers are no longer just asking "how does a transformer work?" or "how do you write a good prompt?"

They want to know if you can architect production-grade multi-agent systems, prevent RAG hallucinations, and manage state across LLM calls.

I’ve been building a visual learning sandbox for multi-agent workflows (agentswarms.fyi), and today I just launched a completely free AI Interview Prep Module inside it.

I compiled 42 top interview questions specifically for GenAI and Agentic AI roles. But instead of just giving a generic answer, the module breaks down the "Standout Answer" and teaches you the mental model of how to answer it like a senior architect.

Here are two examples from the list:

Question 1: When would you use a Multi-Agent Swarm instead of a single LLM with multiple tools?

• ❌ The average answer: "When the task is too complex, multiple agents are better than one."
• ✅ The standout answer: "You use a swarm to prevent context dilution and enforce the Principle of Least Privilege. If you give one 'God Agent' 15 tools and a 4k-word system prompt, its reliability drops and hallucination risk spikes. By routing to specialized sub-agents with narrow instructions (e.g., separating the 'Data Extraction Agent' from the 'Customer Chat Agent'), you isolate failure points and allow for parallel execution."

Question 2: How do you handle hallucinations in a financial RAG pipeline?

• ❌ The average answer: "I would lower the temperature to 0 and give it a better system prompt."
• ✅ The standout answer: "I would decouple data extraction from text generation. I'd use a deterministic node or a strict JSON-enforced agent to only extract the hard numbers from the retrieved context. Then, I would pass that structured data to a separate Synthesis Agent. Finally, I'd implement an 'LLM-as-a-judge' evaluation loop before returning the final output to the user."

What's in the full list? The 42 questions cover:

• RAG Architecture & Vector Databases
• Agentic Routing (ReAct vs. Planner-Executor)
• Evaluation metrics for non-deterministic outputs
• Security (Prompt injection prevention in multi-agent loops)

You can read through all 42 questions, answers, and the "how to answer" breakdowns right in the dashboard here: https://agentswarms.fyi/interview-questions

For those of you who have interviewed for AI Engineering roles recently, what is the hardest system design question you've been asked? I'd love to add it to the list.

Hi All AI builders! I need your honest feedback on my project : https://agentswarms.fyi - it's the only browser based playground available to build and test multi agent systems!

Hey Everyone,

A lot of you have been playing around with AgentSwarms (the Agentic AI learning platform We've been building). We wanted to add a fast way to test data-analysis without having to build a complex node graph, so We just shipped a dedicated SQL & BI Agent workspace right inside the app.

You can drop in a CSV and just start asking questions about your dataset in natural language.

Here is exactly what the agent does:

• Text-to-SQL: You ask a question (e.g., "What were the top 5 regions by revenue?"), and the agent translates your intent into an exact SQL query to run against your dataset.
• Auto-Visualization: Instead of just spitting out a raw JSON array or a boring text table, the BI agent analyzes the shape of the returned data, synthesizes a natural language summary, and automatically renders the appropriate visualization (bar chart, line graph, pie chart, etc.) right in the chat UI.

Why I built this: I was tired of writing custom Pandas scripts or wrestling with Jupyter notebooks every time I just wanted to quickly visualize a dataset or test an AI's analytical capabilities. This gives you an instant playground to chat with your data and see immediate, visual results.

It's free to play with right in the browser.

I'd love for the data nerds here to try it out. What kind of complex aggregations or data questions do you usually struggle to get AI to answer correctly?

Link: https://agentswarms.fyi/data-sql

Hey Everyone,

Over the last few months, I noticed a massive gap in how we learn about Agentic AI. There are a million theoretical blog posts and dense whitepapers on RAG, tool calling, and swarms, but almost nowhere to just sit down, run an agent, break it, and see how the prompt and tools interact under the hood.

So, I built AgentSwarms.

It’s a free, interactive curriculum for Agentic AI. Instead of just reading, you run live agents alongside the lessons.

What it covers:

• Prompt engineering & system messages (seeing how temperature and persona change behavior).
• RAG (Retrieval-Augmented Generation) vs. Fine-tuning.
• Tool / Function Calling (OpenAI schemas, MCP servers).
• Guardrails & HITL (Human-in-the-Loop) for safe deployments.
• Multi-Agent Swarms (orchestrators vs. peer-to-peer handoffs).

The Tech/Setup: You don't need to install anything or provide API keys to start. The "Learn Mode" is completely free and sandboxed. If you want to mess around with your own models, there's a "Build Mode" where you can plug in your own keys (OpenAI, Anthropic, Gemini, local models, etc.).

I’d love for this community to tear it apart. What agent patterns am I missing? Is the observability dashboard actually useful for debugging your traces? Let me know what you think.

Hey everyone,

There is a massive disconnect right now between what indie devs are building with AI (mostly simple customer support chatbots) and what enterprise companies are actually deploying in production (complex, multi-agent swarms).

I wanted to bridge this gap, so I spent the last few weeks analyzing case studies from massive tech companies to understand their multi-agent routing logic. Then, I recreated their architectures as runnable visual node-graphs inside agentswarms.fyi (an in-browser agent sandbox I’ve been building).

If you want to see how the big players orchestrate agents without having to write 1,000 lines of Python, I just published 5 new industry templates you can run in your browser right now (All the examples use Gemini Flash 3.1 preview or Gemini 2.5 pro as a default model):

1. 🛡️ Insurance: Auto-Claims FNOL Triage Swarm

• Inspired by: Lemonade’s AI Jim, Tractable AI (Tokio Marine), and Zurich GenAI Claims.
• The Architecture: A multimodal swarm where a Vision Agent assesses uploaded images of car damage, a Policy Agent cross-references the user's coverage database, and a Fraud-Detection Agent flags inconsistencies before routing to a human adjuster.

2. ⚙️ Manufacturing: Quality / Root-Cause Analysis Swarm

• Inspired by: Siemens Industrial Copilot, BMW iFactory, Foxconn-NVIDIA Omniverse.
• The Architecture: A sensor-data ingest node triggers a diagnostic swarm. One agent pulls historical maintenance logs via RAG, while a SQL Agent queries the parts database to identify failure patterns on the assembly line.

3. 🔒 Cybersecurity: SOC Alert Triage & Response

• Inspired by: Microsoft Security Copilot, CrowdStrike Charlotte AI, Google Sec-Gemini.
• The Architecture: The ultimate high-speed parallel routing swarm. When an anomaly is detected, specialized sub-agents simultaneously investigate IP reputation, analyze the malicious payload, and draft an incident response ticket for the human SOC analyst to approve.

4. 📚 Education: Adaptive Socratic Tutor & Auto-Grader

• Inspired by: Khan Academy Khanmigo, Duolingo Max, Carnegie Learning LiveHint.
• The Architecture: A strict "No-Direct-Answers" routing loop. The Student Agent interacts with the user, but its output is constantly evaluated by a hidden "Pedagogy Agent" that ensures the AI is guiding the student to the answer via Socratic questioning rather than just giving away the solution.

5. 📦 Retail/E-commerce: Returns & Reverse-Logistics Swarm

• Inspired by: Walmart Sparky, Mercado Libre, Shopify Sidekick.
• The Architecture: A logistics orchestration loop that analyzes a customer return request, checks inventory levels in real-time, determines if the item should be restocked or liquidated (based on shipping costs vs. item value), and autonomously issues the refund.

How to play with them: You don't need to spin up Docker containers or wrangle API keys to test these architectures. You can load any of these 5 templates directly into the visual canvas, see how the data flows between the specialized nodes, and try to break the routing logic yourself.

Link: https://agentswarms.fyi/templates

Hey Everyone,

If you’ve ever tried to dump an Apple or Nvidia earnings transcript into an LLM and asked it for a summary, you know it usually messes up the forward-looking guidance or misses the nuance in the Q&A session. A single prompt just can't handle dense financial reasoning reliably.

I’ve been building AgentSwarms (agentswarms.fyi)—an in-browser sandbox for routing multi-agent workflows—and I wanted to test it on a high-stakes financial use case.

In the video, you can see the Earnings Call Analyst Swarm running. Instead of one model doing everything, the workflow is split:

• The Number Extractor
• The Tone Analyst
• The Risk Analyst
• The Compliance reviewer

Why visual routing matters: When you code this in Python, debugging a hallucinated number is a nightmare. In the visual canvas, you can literally click on the edge connecting the nodes and see exactly what the Data Node sent to the Orchestrator.

If you are trying to build financial AI tools, or just want to see how agents can pass data to each other without Python boilerplate, I'd love for you to try this template out in the browser.

Link: https://agentswarms.fyi/templates

AgentSwarms now has a built in prompt comparison lab. Try your prompt outputs simultaneously between Gemini and Open AI models: https://agentswarms.fyi/prompt-compare

AgentSwarms now has a built in prompt comparison lab. Try your prompt outputs simultaneously between Gemini and Open AI models: https://agentswarms.fyi/prompt-compare

Hey Everyone,

If you look at the AI education space right now, it’s flooded with basic "Prompt Engineering" certificates that you can pass just by knowing what a system prompt is. But as anyone building in production knows, chatting with an LLM is 1% of the work. The real nightmare is orchestration, state management, tool execution, and guardrails.

To create a real benchmark for developers, we just launched the Agentic AI Practitioner Exam on agentswarms.fyi. And it is completely free.

Why this isn’t a standard certification: You cannot guess your way through this. To get the certification, you have to pass two phases:

1. The Theory (50 MCQs): Covering the actual hard stuff. (e.g., Memory STM windowing, Text-to-SQL AST validation, A2A handoffs, and production tracing/evals). You need an 80% to pass.
2. The Hands-On Evaluation: This is the gauntlet. The system physically evaluates your sandbox environment. You must successfully build and deploy 5 working agents and 2 multi-agent swarms from scratch (using templates results in an automatic fail).

What the curriculum covers:

• All 7 Agentic Patterns: (ReAct, planner-executor, reflection, routing, parallel, HITL, RAG)
• Production Guardrails: (PII filtering, prompt injection defense, schema validation)
• Multi-Agent Swarms: (Orchestrator, peer-to-peer, and agent-to-agent handoffs)
• Responsible AI: (NIST AI RMF & EU AI Act compliance)

If you fail, there is a 15-day cooldown, and your next attempt will draw from a completely different set of questions. If you want to get another early attempt, you can contribute to the community by publishing your agents and swarms and get free re-attempts!

If you think you know how to build autonomous agents, I challenge you to take the exam and try to pass on your first attempt. Let me know which section of the exam feels the hardest!

Link to take the exam: https://agentswarms.fyi/certification

Hi All AI builders! I need your honest feedback on my vibecoded project : https://agentswarms.fyi - it's the only browser based playground available to build and test multi agent systems!

Hey Everyone,

If you look at the AI education space right now, it’s flooded with basic "Prompt Engineering" certificates that you can pass just by knowing what a system prompt is. But as anyone building in production knows, chatting with an LLM is 1% of the work. The real nightmare is orchestration, state management, tool execution, and guardrails.

To create a real benchmark for developers, we just launched the Agentic AI Practitioner Exam on agentswarms.fyi. And it is completely free.

Why this isn’t a standard certification: You cannot guess your way through this. To get the certification, you have to pass two phases:

1. The Theory (50 MCQs): Covering the actual hard stuff. (e.g., Memory STM windowing, Text-to-SQL AST validation, A2A handoffs, and production tracing/evals). You need an 80% to pass.
2. The Hands-On Evaluation: This is the gauntlet. The system physically evaluates your sandbox environment. You must successfully build and deploy 5 working agents and 2 multi-agent swarms from scratch (using templates results in an automatic fail).

What the curriculum covers:

• All 7 Agentic Patterns: (ReAct, planner-executor, reflection, routing, parallel, HITL, RAG)
• Production Guardrails: (PII filtering, prompt injection defense, schema validation)
• Multi-Agent Swarms: (Orchestrator, peer-to-peer, and agent-to-agent handoffs)
• Responsible AI: (NIST AI RMF & EU AI Act compliance)

If you fail, there is a 15-day cooldown, and your next attempt will draw from a completely different set of questions. If you want to get another early attempt, you can contribute to the community by publishing your agents and swarms and get free re-attempts!

If you think you know how to build autonomous agents, I challenge you to take the exam and try to pass on your first attempt. Let me know which section of the exam feels the hardest!

Link to take the exam: https://agentswarms.fyi/certification

Hey everyone,

If you’ve been building with AI agents, you know that orchestrating text is one thing, but stepping into multimodal workflows (Text + Image + Vision) is incredibly messy.

If you want an agent to act as a "Prompt Engineer," pass that prompt to an "Image Generator," and then have a "Vision Agent" critique the output to force a re-roll—you are looking at hundreds of lines of Python boilerplate, messy API handshakes, and a terrible debugging experience when the loop breaks.

I recently launched agentswarms.fyi, an in-browser sandbox for learning Agentic AI. Today, I am pushing a massive update: The Image Playground.

What the feature actually does: Instead of fighting with code to test multimodal architectures, you can now drag, drop, and wire up text and image agents on a visual canvas to build creative workflows.

• Image Generation Nodes: Wire any text-output agent directly into an Image Node to autonomously generate visual assets.
• Vision AI Integration: Route generated images back into a Vision Node. You can instruct an agent to physically "look" at the generated image, evaluate it against your initial prompt, and trigger a loop to fix it if it hallucinated.
• Real-Time Data Flow: You can actually watch the payloads (the text prompts and the image outputs) flow across the node graph in real-time.

Hey Everyone,

If you’ve been building with AI agents, you know that orchestrating text is one thing, but stepping into multimodal workflows (Text + Image + Vision) is incredibly messy.

If you want an agent to act as a "Prompt Engineer," pass that prompt to an "Image Generator," and then have a "Vision Agent" critique the output to force a re-roll—you are looking at hundreds of lines of Python boilerplate, messy API handshakes, and a terrible debugging experience when the loop breaks.

I recently launched agentswarms.fyi, an in-browser sandbox for learning Agentic AI. Today, I am pushing a massive update: The Image Playground.

What the feature actually does: Instead of fighting with code to test multimodal architectures, you can now drag, drop, and wire up text and image agents on a visual canvas to build creative workflows.

• Image Generation Nodes: Wire any text-output agent directly into an Image Node to autonomously generate visual assets.
• Vision AI Integration: Route generated images back into a Vision Node. You can instruct an agent to physically "look" at the generated image, evaluate it against your initial prompt, and trigger a loop to fix it if it hallucinated.
• Real-Time Data Flow: You can actually watch the payloads (the text prompts and the image outputs) flow across the node graph in real-time.

Hey everyone,

If you’ve been building with AI agents, you know that orchestrating text is one thing, but stepping into multimodal workflows (Text + Image + Vision) is incredibly messy.

If you want an agent to act as a "Prompt Engineer," pass that prompt to an "Image Generator," and then have a "Vision Agent" critique the output to force a re-roll—you are looking at hundreds of lines of Python boilerplate, messy API handshakes, and a terrible debugging experience when the loop breaks.

I recently launched agentswarms.fyi, an in-browser sandbox for learning Agentic AI. Today, I am pushing a massive update: The Image Playground.

What the feature actually does: Instead of fighting with code to test multimodal architectures, you can now drag, drop, and wire up text and image agents on a visual canvas to build creative workflows.

• Image Generation Nodes: Wire any text-output agent directly into an Image Node to autonomously generate visual assets.
• Vision AI Integration: Route generated images back into a Vision Node. You can instruct an agent to physically "look" at the generated image, evaluate it against your initial prompt, and trigger a loop to fix it if it hallucinated.
• Real-Time Data Flow: You can actually watch the payloads (the text prompts and the image outputs) flow across the node graph in real-time.

Hey everyone,

If you’ve been building with AI agents, you know that orchestrating text is one thing, but stepping into multimodal workflows (Text + Image + Vision) is incredibly messy.

If you want an agent to act as a "Prompt Engineer," pass that prompt to an "Image Generator," and then have a "Vision Agent" critique the output to force a re-roll—you are looking at hundreds of lines of Python boilerplate, messy API handshakes, and a terrible debugging experience when the loop breaks.

I recently launched AgentSwarms, an in-browser sandbox for learning Agentic AI. Today, I am pushing a massive update: The Image Playground.

What the feature actually does: Instead of fighting with code to test multimodal architectures, you can now drag, drop, and wire up text and image agents on a visual canvas to build creative workflows.

• Image Generation Nodes: Wire any text-output agent directly into an Image Node to autonomously generate visual assets.
• Vision AI Integration: Route generated images back into a Vision Node. You can instruct an agent to physically "look" at the generated image, evaluate it against your initial prompt, and trigger a loop to fix it if it hallucinated.
• Real-Time Data Flow: You can actually watch the payloads (the text prompts and the image outputs) flow across the node graph in real-time.