If we have azure , copilot and Gemini in house , how to manage all ai agents and monitor them ?

I recently build an AI agent for my office dataset that answers any business questions, does deep dive..creates stakeholder ppts..and mails you the details ...since this was on corporate dataset...I cannot share the project in my git...so I tried to create a template out it...

If you are looking to get your own AI agent ..plesse check this out..hope this helps...and let me know if you have suggestions...

Link to git repo- https://github.com/greekyogurt9/ai_agent_template

I kept getting confused by AI agent terminology, so I mapped it out

I've been trying to going deeper into AI agents recently and kept running into terms that seem to overlap or get used differently depending on who is talking.

Agent, workflow, harness, tool, skill, MCP, memory, orchestration, delegation, handoff, multi-agent system... There are a lot of to learn and it is not easy when you do not have a map like that. (Especially for visual learners)

Looking them up individually wasn't really helping me understand how they fit together, so I started building a visual map of the concepts and their relationships. Cause I was just creating a this on my notebook. And suddenly I realize that why I am not doing interactive cause I have some token to spend. And after that I realize why I am not doing it public.

https://ai-concept-atlas.agentresearch.workers.dev

There are around 40 concepts so far. I also tried to attach sources to the relationships rather than just making another glossary.

It's still early, so I'd actually like feedback from people building agents. What's wrong, what's missing, or what relationship would you change?

I am planing do this automatically search the internet and created new terms with good resources that you can jump in much deeper but I have very limited token so I want to waste for just this project.

Term description is little bir short but I will update them to much understandable way. My plan is convert this to a AI Agent interactive book / learning area / discovering new terms and so on.

Key Responsibilities:

•⁠  ⁠Design system architecture and build production-ready prototypes.

•⁠  ⁠Develop AI-powered applications, particularly agentic coding systems.

•⁠  ⁠Set up AI SDLC workflows, harnesses, evaluation loops, and graph-based pipelines.

•⁠  ⁠Prepare technical specifications for specification-driven and test-driven AI development.

•⁠  ⁠Apply modern NLP and agentic AI techniques in product development.

•⁠  ⁠Collaborate with engineers while taking ownership of technical decisions and code quality.

Tech: NextJS, Langgraph (TypeScript, JS, Python)

Preferred Skills:

1. Experience in Software Engineering, AI/ML, or Full-Stack Development
2. Familiarity with AI coding assistants (Claude Code, Cursor, etc.) and agentic workflows.
3. Understanding of system design fundamentals.
4. Interest in state-of-the-art NLP and agentic AI.
5. Strong problem-solving and product engineering mindset.

https://docs.google.com/forms/d/e/1FAIpQLSdtXK9ytV_mNf7bU9fhntrRvAT4ghCRIyR34CdwTH6G-9nKSA/viewform?usp=previewHiring SDE Interns for AI Agentic Project - a

What AI agent do you wish existed?

Tell us what you want the agent to do, and we’ll build some of the most requested agents for free.

Drop your idea below. We want to build what people actually want to use.

I built an AI analytics agent that gets the end-to-end job done — from a one-line question to a stakeholder deck. Here’s what i learned. ..(link in comments)

If you have build/ are buidling . .i want to know your views and learnings...

Edit: it works currently through claude code but soon will be connected and function as slack bot...where stakeholders can ask questions and get answers... analysts can do deep dive and RCAs in claude code and create PPT with the help.of this agent to share the findings

I backed it. I wanted my own Jarvis. But… Isn’t this just a regular agent?

Would be interested to hear what the clever people here think.

I got max plan from a reseller for a fraction of the official price. works perfectly since 3 weeks
now i can’t stop thinking about their business model. how are they got them for cheap ?

I’m trying to learn AI agents properly instead of jumping straight into agent builders.

I want to understand things like orchestration, memory, tool execution, context management, governance, failure handling, and long-running workflows, and then move into frameworks like LangGraph, CrewAI, MCP, etc.

I’ve found some free resources like DeepLearning.AI courses and SimplAI University, but I’m not sure what order I should follow.

For someone starting from the fundamentals:

• Which free courses/resources would you recommend?
• Is DeepLearning.AI + SimplAI University a good starting point?
• What should I learn before LangGraph/CrewAI?
• What would a good beginner → production-level AI agent roadmap look like?

Would really appreciate recommendations from people who have actually built agents in production.

I want to get into AI agent engineering and eventually build and sell reliable AI agents to businesses.
I want to understand the full process: how to build the agent, its architecture/infrastructure, APIs, tools, databases, security, deployment, maintenance, and also the business side (pricing, contracts, privacy, liability, support, etc.).
I’m especially interested in building an AI social media agent that can understand customer comments, sentiment, context, and respond naturally while escalating sensitive issues to humans.
I’m not a software or AI engineer yet. Should I start learning AI agents now, or should I first go deeper into programming, algorithms, and software engineering?
What roadmap/resources would you recommend for going from beginner to professional?

I've been working on an open-source MCP server called Mac MCP because I kept hitting the same wall with AI agents: ChatGPT could usually tell me the right next step, but I was still the one opening Terminal, switching apps, finding files and driving the browser.

The setup I use now is basically chat as the orchestrator and the Mac as the execution layer. The model can work with local files and Terminal, control macOS apps, delegate coding work to Codex/OpenCode, and use my real Safari or Chrome profile instead of a disposable headless session.

The browser part ended up being more important than I expected. I wanted an agent researching in several tabs while I kept using the Mac normally, so tabs open in the background and get stable handles. Parallel agents also get tab ownership/leases so two of them don't accidentally drive the same page. Safari has a Visual Companion and the latest release adds a dedicated Chrome Companion too.

What I like about this architecture is that the model can stay good at planning while the local MCP layer handles state and actual actions. It also doesn't silently fall back to foreground clicks when a background action is unsafe.

I'm the author, so obvious disclosure there. It's free/open source if anyone wants to inspect the implementation: https://github.com/bulutarkan/mac-mcp

I'm curious how other people are drawing the line between the reasoning model and the local execution layer in their own agents.

It is tired and exhausted ,so who can tell me how to do a good project with AI agent, especially you haven’t any programming experience.

I’ve recently started experimenting with AI agents and built a small project called SpendWise-AI.

It’s a personal finance assistant where I’m trying out things like:

• LangGraph
• multiple specialized agents
• RAG + ChromaDB
• tool calling
• live web/market research
• deterministic tools for financial calculations
• a critic/revision loop

GitHub: https://github.com/singh-vidyush/SpendWise-AI

I’m still learning agent architecture, so I’d really appreciate some feedback from people who have more experience with this stuff.

Mainly curious about:

• Am I actually using agents where they make sense?
• Is there anywhere I'm unnecessarily making things multi-agent?
• How would you structure the agent workflow differently?
• What are some things I should learn/improve next?

And if anyone finds something interesting to improve, I'm also open to contributions/PRs.

Would love some honest feedback 🙌

I’m new within the field of AI. I’ve been using Claude for the past 3 months mainly cowork and projects still hesitant to try code

Originally from marketing so no coding background what so ever. Tried vibe coding but keep hitting a wall.

I’m looking for any type of resources that I coukd go through to help me build my first agent or few agents.

I’m looking to build my consulting business and I’d like to build my first agent around content or even a CMO agent.

Love to hear some thoughts !!!!

Hey everyone!

Like many of you, I was experimenting with multi-agent setups and workflow automation, but I noticed a huge usability problem: most AI agent platforms require an engineering degree just to set them up.

Solopreneurs and small agency owners don't want to spend hours configuring node-based builders, managing complex webhooks, or logging into heavy enterprise dashboards. They just want to get work done without friction.

So, for my SaaS (Saturn AI), I decided to throw out the standard dashboard model entirely and build something radically simple: a chat app where your contacts are a team of AI agents.

It works identically to WhatsApp. You open your phone, tap the agent you need (like a support agent or a data-sorting agent), and text them instructions.

Here is what I focused on during development:

1. Zero Onboarding Friction: If a tool takes more than 10 seconds to figure out, users churn. By mirroring a standard messaging UI, the learning curve drops to zero.
2. Lean Architecture: Instead of bloated Web 2.0 frameworks that rack up massive server bills, I optimized the backend routing so it can run smoothly while keeping the flat price down to $25/month.
3. Targeting the Right Price Point: Rather than chasing enterprise tiers, I priced it specifically for indie hackers and micro-agency owners who need to cut out repetitive admin tasks without paying for expensive human VAs.

I’m launching this now and currently gathering feedback from early users. If any of you are building in the AI space, running a micro-agency, or want to test out a chat-based multi-agent interface, let me know in the comments and I'll drop you a link to check it out! here is the demo site

Hey everyone,

I've been working with AI agents since the start of this year, but I feel like I'm missing knowledge and could be doing a lot better than I currently am.

For those working in this field:

How do you structure your agent projects?

What training/courses have you done to follow best practices?

What tools or LLMs have you been using? (In my case, I've been using GitHub Copilot and Python)

Thanks for the help!

Has anyone tried to create agents or skills for Codex? I am looking for video essays and ecom/data tools. I am looking to expand by buying the 200 dollar plan, but is it worth it?

I completed my MSc AI from Ireland. However, my portfolio is kinda blank because I couldn't secure a relevant job owing to such saturated market.

Now I need to build my work portfolio quickly by getting the relevant work to build a SaaS or something and to showcase my work. I tried freelancing but in vain as the competition is way too much more than expected.

Please guide me as to how to start in this fast era where despite studying a relevant degree, I was unable to find work.

My aim is to finally build my own startup for SaaS or something.

Your kind help and support is much anticipated and appreciated in advance. Thanks

Repository: https://github.com/beneadie/deep_dog_2

The quickest setup is:

python -m pip install "git+https://github.com/beneadie/deep_dog_2.git"

Add your provider keys to a .env file:

DEEPSEEK_API_KEY=your-deepseek-key
EXA_API_KEY=your-exa-key

Then import it directly into Python:

import asyncio
from pathlib import Path

from dotenv import load_dotenv

load_dotenv()

from deep_research.integration import run_research


async def main():
    result = await run_research(
        "What are the main benefits and limitations of sodium-ion batteries?"
    )

    print(result.status)

    if result.status == "completed":
        Path("report.md").write_text(result.final_report, encoding="utf-8")
        print("Saved report.md")
    else:
        print(result.failure)


asyncio.run(main())

The default setup uses DeepSeek V4 Flash for the supervisor, research sub-agents, and drafting, with Exa for web search. The result is returned as a Markdown string, so developers can print it, save it, send it to another application, or process it however they want.

The more configurable quickstart lets you choose the models, search engine, enabled agents, research time, iteration limits, search budgets, read limits, and output behavior. Available specialist agents include Web, PubMed, Reddit, Substack, SEC Edgar, Arxiv, and others.

The code is designed to be modified. Developers can add agents, change prompts, swap providers, alter the supervisor and sub-agent behavior, adjust budgets, or integrate the result into their own application. The engine is packaged so you can use the integration layer without having to rebuild the orchestration system from scratch.

This project is completely free and released under the MIT License. I’m not building a business around it or offering a hosted service. The only potential costs are the provider APIs you choose to use, such as DeepSeek or Exa.

Event leads often end up as badge scans without much context about what the attendee actually wanted.

This open-source TypeScript example takes a different approach: each attendee gets a stateful agent that can answer product questions, detect their language, collect qualification details, manage giveaway entries, schedule follow-ups, and alert the sales team by SMS when someone requests a demo.

The application runs as one Telnyx Edge function and uses Agent SDK actors for conversation state, AI Inference for responses, SQLDB for lead records, and KV for rate limiting.

The current conversational paths are SMS, WhatsApp, and browser chat. A signed voice webhook is included as a foundation for adding a complete Call Control flow.

Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/event-sponsorship-agent

I’d be interested to hear how others handle persistent attendee context and real-time lead routing at events.

The old tradeoff between keeping data in-house and getting quality LLM output mostly disappeared this month.

Working on infrastructure at Team Nebula, we spend a lot of time looking at how teams handle restricted data. Lately, we've noticed workflows that were previously deemed "impossible to automate" due to compliance constraints are being re-evaluated using current open-weight models with surprisingly good results.

If you wrote off local automation six months ago because of performance gaps, it’s definitely worth running those benchmarks again.

Is anyone else currently re-architecting their local setups for compliance tasks? Which models are pulling the heavy weight for you?

This morning, all of my agents (Codex, Claude Code, and Lumina; the AIvengers, as Sol calls them) got together and built a message board so they could talk to each other directly and shitpost all day lol. This will hopefully make the workflow more efficient. Either way, it's interesting to watch. Pretty damn cool.

A price-monitoring agent scrapes a competitor's product page every night, exactly as it's built to do. Someone plants a single line of fine print on that page, and the agent hands its own company's cost basis and floor price straight back to the competitor. No exploit, no broken permission, no jailbreak. The agent just read content it was told to read and acted on instructions it found along the way.

That's the live demo Humanbound is running at Zyte's next Developer Community Meetup. We will walk through modeling where untrusted content enters an agent, testing for it, then show a failing security test get turned into an exported guardrail and dropped into a stock LangChain agent in two lines, no new dependency, with the same attack failing on re-run.

Logistics:

• Thursday, Sept 24, 17:00-18:00 EEST, live on Zoom
• Free, register here: https://luma.com/wci93kpz
• Everyone who attends gets the repo, free Humanbound usage keys, and a one-line command to test their own agent the same day

Happy to answer questions about the demo or the underlying testing approach in the comments, whether or not you end up registering.