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AI Agent Memory Validation Platform — continuously verify whether facts stored in AI agent memory systems (Mem0, Zep, Letta, LangMem) are still true. Like Datadog for agent memory.
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I built an AI agent that handles customer operations — refunds, order lookups, and support tickets — with a human approval gate built into the workflow. The agent proposes the action, pauses, and waits for a human to approve before anything executes. The LLM never makes the final call. Role-based access and a full audit trail are enforced in code, not prompts. Deployed live on Azure Container Apps.
The pause for human sign off keeps refunds safe while still offloading the lookup work to the agent.
What if every new lead could be handled before you even open your inbox? ⚡
This AI-powered workflow turns a new email inquiry into a structured, ready-to-handle lead — automatically.
A new inquiry arrives → AI extracts the important details → Airtable/CRM is updated → the response goes through optional human approval → a personalized email is sent → Notion is updated → the team gets notified in Slack → performance data is collected for reporting.
The goal isn't to remove people from the process. It's to remove the repetitive work around them.
This type of workflow can be customized for B2B companies, SaaS businesses, agencies, real estate, e-commerce, recruitment, consulting, customer support, healthcare, finance, education, and other service businesses.
Already know what you want to automate? → Send me your current workflow and I’ll map out how we can automate it.
Still doing repetitive work manually? → Tell me the task that consumes your team’s time, and I’ll help identify what can be automated.
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I'd use the human approval step to capture corrections, not just a yes/no decision. If a reviewer changes the extracted lead details, does the workflow update Airtable and the reporting record before sending the email?
Testing my app! 🧰🚀 Everything is finally coming along.
I’ve been working on this project for a little over two weeks. Initially it was just me working with one AI agent, but I was still manually coding most of it, so I was moving like a turtle 🐢very slow.
Then I decided to implement Codex into my project to automate more of the development and it has been amazing. Now I can focus more on prompting, testing, reviewing the code and making the technical decisions instead of manually coding everything.
I also created my own AGENTS.md bible with all my project rules and instructions to make sure Codex follows them as the final authority when working on my code.
The idea is simple: create one useful and intuitive ecosystem of tools that Shopify merchants can add to their stores to help customers make purchasing decisions faster and with more confidence.
Still testing, building and adding more tools. Let’s see where this goes! 🚀
And if anyone finds the idea interesting. I’m definitely open to collaborations, ideas or partnerships. Feel free to reach out!
Seeing you shift from hand coding to Codex and let the agent handle the grunt work really speeds up iteration.