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Eric
133 posts
@log_npierce Exactly. The wrapper gets attention, but permissions are where the product either becomes useful or dangerous. The interesting part is making agents powerful enough to do real work while still being scoped, reviewable, and easy to shut down when something looks wrong.
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@ericcco_ permissions and orchestration are the real bottlenecks right now. shipping a wrapper is easy, making it survive a real production workflow with actual security constraints is the hard part.
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@twschiller This is super relevant. Browser agents make permissions, identity, and auditability matter immediately. Curious how you draw the line between attended and unattended use, especially when the agent can touch real accounts or sensitive data.
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@ericcco_ Working on a couple complementary projects:
- Agentic browser copilots: pixiebrix.com
- Protecting browser use agents (attended and unattended): github.com/pixiebrix/agen…
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@log_npierce Yes, setting the correct boundaries allow you having control over your workflows
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@ericcco_ context is everything. most "ai" features today are just expensive noise because they lack the execution boundaries to be actually useful in a real workflow. human-in-the-loop is the only way to scale agents without losing control
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AI replies are not the problem.
Low-context, unsupervised AI replies are the problem.
The future is not “let bots flood every conversation.”
It’s agents that understand the context, know the goal, stay within boundaries, and make it easy for a human to approve or correct the output before it goes live.
Automation without control creates spam.
Automation with context creates leverage.
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@ericcco_ Hello Eric
I'm an AI Automation Engineer. With AI-Powered product (SaaS)
Looking forward to connect and learn together
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Good point. Integration with existing systems is a must, especially in regulated industries where compliance is non-negotiable.
Building agents is getting easier and faster, but having the right guardrails, governance, and control over how they operate is what will make them enterprise-ready.
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This is the layer we're focused on too.
If agents are going to replace legacy workflows, they need more than orchestration.
They need integration with the systems where work already happens, compliance around what data can move, and communication between agents that is identity-aware, scoped, and auditable.
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@m13v_ @flytradr_guy Totally. The first version is the easy part now. The harder part is keeping the workflow useful once people start changing it, approving things, fixing failures, and relying on it every day. That’s where you find out if it’s real infrastructure or just a good demo.
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@flytradr_guy @ericcco_ agent demos in a workflow always land clean. the gap you're naming isn't the framework, it's iteration under change, where the AI-built first draft holds up or collapses into debt once approvals and failure handling get bolted on. mk0r.com/r/zmd26u6u written with ai
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@ericcco_ Building a Typescript specialized harness around Qwen 3.6 27B. I want to see whether I can get it to flagship quality by keeping it very scoped and specialised. Using DGX Spark and running tests 24/7 in a self corrective loop.
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@ericcco_ Hi Eric I’m building AI agents for my projects recent one
Dhilip Subramanian@sdhilip
Built an AI-powered Document Intelligence Review Workbench for a manufacturing client in the US. The problem was simple: Teams were dealing with large volumes of PDFs, scanned documents, internal policies, supplier docs, external links, and operational records. Manual search was slow, and every answer needed to be backed by source references. So I built an end-to-end RAG solution on Azure. Architecture: • Azure Blob Storage for document storage • Azure AI Document Intelligence for OCR • Azure AI Search for vector + semantic retrieval • Azure Functions for the API layer • Azure AI Foundry for model orchestration • GPT-5.5 and Claude model selection • React frontend for upload, review, citations, and follow-up chat Flow: Upload document → OCR/text extraction → retrieve relevant knowledge → generate structured summary → show findings with citations → ask follow-up questions. I also added: • scanned PDF support • citation links • model switching • clean review dashboard • non-relevant document detection • follow-up chat grounded in uploaded documents and retrieved sources This was a fun full-stack AI build covering RAG ingestion, Azure architecture, backend APIs, LLM integration, OCR, and frontend UX. The key point: AI is useful only when the answer can be traced back to the source.
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@Lakshman2302 @GrayCodeAI Love the “humans and AI agents build together” framing. Are you focusing more on orchestration, collaboration UX, or review/control?
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@flytradr_guy This is exactly the gap I’m interested in. Once agents touch production workflows, approvals, auditability, and failure handling become the real product. Would love to compare notes on FlyTradr.
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Most agent demos show the happy path.
The real test is what happens when the tool fails, the context is incomplete, the user changes their mind, or two agents disagree.
Agent reliability won’t come from making them sound smarter.
It’ll come from better recovery loops.
What’s the most underrated part of building reliable agents?
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Enterprise agents won’t fail because they lack “more autonomy.”
They’ll fail when nobody can answer basic questions:
- Who gave this agent permission?
- What systems can it touch?
- Which actions are reversible?
- Where is the audit trail?
- Who owns the outcome when agents coordinate?
The hard part isn’t making agents act.
It’s making their actions attributable, bounded, reviewable, and safe enough for real organizations.
What should enterprises solve first: identity, permissions, auditability, or policy enforcement?
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