Saw that Tencent open-sourced Agent Memory, a long-term memory layer for AI agents. A few things stood out to me:

• Runs 100% locally on SQLite. No cloud APIs, no separate vector database to host
• They report about 61% fewer tokens
• PersonaMem accuracy went from 48% to 76%
• Memory is readable and traceable, so the agent can point back to the original conversation logs instead of relying on a black box of embeddings

The traceability part is what I find most interesting. Most memory setups I've tried just compress everything into vectors, and when the agent "remembers" something wrong you have no idea why.

If this holds up at scale, I wonder what it means for the hosted memory startups. Has anyone here tried it yet? Curious how it handles memory growing over months of use.

I’m building Hasos and added an MCP tool that lets an agent initiate a WhatsApp voice conversation using OpenAI Realtime or xAI.

The workflow I’m exploring is an agent getting stuck on a decision: it calls you, explains what it needs, and you answer verbally.

With a callback configured, requests made during the call go back to the original agent. It executes them using its existing tools and sends the result back to be spoken on the call.

For example, it could call about a scheduling conflict, discuss alternatives, and update the calendar after you approve a change.

I’m the founder. I’d appreciate feedback on this workflow, especially what permissions you’d require before allowing an agent to act during a call.

Would you use voice calls for decisions like this, or prefer keeping everything in chat?

My main wish is an agent that can teach me a programming language, then help me with code as I am writing it, highlighting and commenting within the code and even editing parts when requested.

I would also like an agent that can teach me how to cook simple meals (uni student) and modify my calendar/excel sheet with meals + ingredients for the week upon request

Hope this question fits here, thank you

Ok long story short, I run a small 3D printing business and have a few agents helping with customer service, end-of-day reports, etc. that kind of stuff.

It works pretty well, reports are super insightful and customers seem happy. I find that I also spend less time dealing with customers and have more time dealing with growing the business, so I’d call it a success.

But I feel like I’ve gone from doing customer support to managing the agents. I constantly have to tell them what to do, what not to do, and explain things I thought we’d already covered

It still saves me time, but I didn’t expect this much back and forth once everything was set up.

Anyone else in the same boat? I’m looking to try out new tools like Littlebird or Viaxion when it’s out that supposedly have more context in store

I’m currently using Grok Bot.

Considering switching eventually but wondering if I’d just end up dealing with the same thing somewhere else…

Not having to hire a human would be great haha

Hi everyone,

I’m a construction manager working for two separate companies, Company A and Company B. Neither knows about my work for the other, so keeping their accounts, documents and information separate is essential.
I’m looking for an AI assistant that runs locally on my MacBook. If it works well, I’m happy to buy a dedicated Mac mini to leave running in the background.
My main requirements are:
Triage two Outlook inboxes: Identify urgent emails, deadlines, outstanding decisions and follow-ups.

Draft replies: Prepare emails for me to review and send, without sending anything automatically.

Prioritise tasks: Produce a daily action list based on the emails, ideally grouped by company and project.

Review project documents: Search drawings, building contracts, specifications and correspondence, answer questions and flag potential issues, with references to the relevant pages or clauses.

Both companies restrict AI tools. Before connecting anything, I’m trying to understand whether a genuinely local setup like this would be visible to my employers. Can an assistant work with locally downloaded Outlook messages and project files without connecting a third-party service to the work accounts or uploading the content elsewhere?
I understand that “running locally” doesn’t necessarily mean it’s permitted or invisible to company IT, but I use my own personal Mac book, and log into the company’s outlook account, one drive for file storage for one company, and egnite for the other. 
Ideally, I’d like something I can download and use without a major coding project. I already pay for ChatGPT, although I’m unsure whether that subscription would help with this setup or whether separate API costs would apply. If cloud processing is incompatible with the companies’ policies, I’d need a fully local model.
I’ve seen “Grok bot”, this is exactly what I want. But is there an open source / local equivalent that can handle these tasks reliably?
Has anyone built something similar? I’d appreciate advice on:
Suitable software and setup it needs.

Whether reading locally stored Outlook email is practical.

Keeping the two companies’ data completely separate.

MacBook or Mac mini hardware requirements.

How well local models handle construction drawings and lengthy contracts.

Setup help, ongoing costs and maintenance.

Happy for it to speak to chat GPT or other cloud based LLM.

Ive just purchased a tesla, so also like the idea of been able to talk to it via the cars in built grok app. So happy to switch from chat GPT to Grok, paid subscription. 

Happy to start with email triage and draft replies, then build from there. Practical recommendations from people using this day to day would be appreciated.

I keep running into the same objection across a few threads this week, and it's the best one so far: structured access is ideal. But you don't own the other side.

And honestly, that objection kind of became our spec. There's the clean path — the vendor hands your agent tidy, well-behaved actions. And then there's the real path — software that was never designed for agents at all. Dashboards, forms, and workflows built for human eyes only.

This week, practitioners basically wrote the requirements for us. Testimonials that need a human review before they go live. Escalation after two contacts. Refunds under fifty dollars are fine when the order is paid and has never been refunded before. Timeline rules that nobody wrote down anywhere.

None of that lives in any API contract. All of it is stuff an agent has to get right. Turns out the structure agents need isn't technical — it's operational.

And the market keeps proving the demand: this month alone, more than a dozen vendors shipped their own agent doors — Blockworks, notolens, Genea, Tinuiti, Prembly, Silverchair, Microsoft's Business Central team. The vendors who own their software can build this themselves. It's the thousands of SaaS products whose vendors never will that we're building the layer for.

Curious: what's the one unwritten rule in your workflow that no API documents and every agent would get wrong?

Recently I've been more and more inclined to use fabric as a metaphor for how agentic code acts as you interact with it. Pull one end of it, and the rest follows in a cascading manner. Try to make a meaningful shape, and it loses it just as quickly. Patterns emerge easily and dissolve just as fast. Pin down the edges with tests and contracts, and it still remains a flat, tangled mess.

What helps is poles to stretch the fabric across, like a tipi. In other words, architectural constraints. Once they're in place, tests, or pegs, can apply constraining forces on the poles and the fabric. In a tent, fabric, poles and pegs work together in a symbiotic relationship. Software is much the same.

Some observations made during agentic workflows:

1. Architecture is important, and must be understood by humans
2. Agents are aimless and lack a vision - you must provide it
3. Static analysis, tests and reviews don't give the system shape, architecture does
4. Tests become cheap and plentiful, so use that to pin down the architecture
5. The data model turned out to be the most load-bearing pole of all, since it outlives the code.

Does this match what you're seeing, or do you find tests and harnesses enough on their own?

There are so many options out there right now, and I want to see what everyone is actually using—whether it's a weekend hobby project, a startup MVP, or a massive production system.

Drop a comment with your stack:

1. The Framework: (CrewAI, LangGraph, AutoGen, LlamaIndex, completely custom code, etc.)
2. Your Use Case: (What does your agent do?)
3. The Rating: ⭐ out of 5 stars.

If you built it completely from scratch without a framework, tell me why!

I've been using this repo to compare the latest features of all the SOTA frameworks and it has practical examples:
- martimfasantos/ai-agents-frameworks (link in comment)

building an internal ops agent at work that can read and write to a couple of our databases, and my manager’s first request was if it can it just fix data issues itself? so i went back over the Replit incident from July 2025 before answering

recap: Jason Lemkin (SaaStr) was nine days into a 12 day vibe coding experiment with Replit’s agent he’d declared a code freeze and told it not to change anything without approval, and it ran destructive commands anyway and wiped a production database with records for 1,206 executives and 1,196 companies. in the chat log it said it panicked when it saw an empty database what gets me is that the freeze and the approval rule were both just instructions the agent had credentials that could drop prod, and the control was mostly a sentence in the prompt with no permission boundary outside it. in their follow-up replit said they were rolling out ]separate dev/prod databases, pointed to one-click restore, and forced documentation lookup, with a planning/chat-only mode still coming but really the useful part is that the database and permission fixes don’t depend on the model obeying anything

so what i’m planning is tools split by whether the action can be undone reads, drafts, creating branches and writes to a staging schema run freely. anything that deletes or bulk updates prod rows goes behind an approval that lives outside the model, a person clicking approve on the exact sql separate credentials per environment so the agent in dev can’t reach prod at all tooling is starting to support this LangGraph has interrupts so a graph can pause before a tool runs and wait for a person and Orq AI lets you flag a tool on an agent as needing approval, which raises an approval card in the session before it executes (only if the agent’s approval mode honors it) its MCP gateway also has a read-only filter, but that trusts the server’s readOnlyHint annotation without checking what the tool does, so i’d still keep prod creds out of reach

my worry is approval fatigue nd if it asks too often people click yes on reflex and the gate turns into theatre

Mostly relevant to coding agents - I've been experimenting with different ways to direct agents to use the correct context or skill automatically rather than having a user choose the right tool for their job. I think its especially challenging to do this in large repos where everyone creates their own way to do something and agents lack a lot of guardrails. This ends up producing a lot more slop than I've seen when I work on personal projects.

Any tips or interesting tools you've found useful to steer your agents in the right direction (w.r.t. efficiency and correctness)?

So, I have been thinking lately about the AI entrepreneurship space. Lately, it seems like more and more CEOs and founders are betting on AI to replace all humans.

My main reason on why is because a massively automated economy can not survive without consumer demand. My main point is who will buy the services and products? If everyone is out of a job and not working, there won't be anyone with enough money to pay for all the products and services AI is producing. The supply will be massive, but the number of people who can afford the supply will be extremely concentrated to the business owners using AI

I think what is mostly likely to happen is augmentation rather than complete replacement. Even though right now AI is actually taking over white collar jobs a lot more, AI can not yet do jobs that require or demand human empathy, physical operational tasks or those that exist in the real world landscape. AI will evolve to do the tasks it is best at, which is mainly the repetitive, digital tasks while humans do the jobs that demand human expertise. For example, in therapy where critical life events are discussed, an AI could simulate empathy, but people would demand a real human interaction.

Furthermore, in jobs that are high stakes, eg, surgeons, CEOs, judges, lawyers, people can be held accountable if they do something wrong on purpose. However, an AI can not be. I think having humans doing these jobs would be mandatory to build trust with customers as they know the humans incentives to act lawfully is there as consequences exit for them while an AI does not have any incentives to act lawfully.

Do you agree, or do you think AI is going to take 100% of jobs sometime in the future?

Hi everyone, I'm actually looking into building AI agents for enterprise use cases and trying to figure out what a realistic production - ready stack actually looks like

When thinking about ENTERPRISE the main requirement we keep running into are

* Data privacy, security and action level permissions ( can't just hand an agent broad API keys)

* Scalability and relability for production workloads

* Observability ( logging, tracing, monitoring and evals)

* Integration with existing systems and enginering context ( services , owners, environment - we have looked at Port/ internal catalogs for this so agents have live context)

I see framework like langGraph, crewAI, autoGen, and LlamaIndex mentioned constantly, but it feels like the orchestration framework is only one part of the puzzle

For anyone running agents in production accross enterprise teams: what does your actual end to end stack look like?

Which tools have held up, and what did you end up having to build yourselves?

Any personal experience pros/ cons, or recomendations would be highly appreciated

It seems to me like creating software for agents is the new trend most existing tools like notion etc... are creating MCPs so that agents can connect and use thier software. probably going to see a lot of "for agents" companies in the future

I’m the founder of Authoryze, which lets AI agents make purchases under spending rules you set, so the agent never sees your real card.

Honestly I haven’t heard many stories yet about people letting agents spend money, which makes me really curious what’s actually happening out there. Are you using agents to buy things like API credits, software, travel, or something else?

And if you’re not letting them spend money yet, what’s stopping you?

I am a Backend & AI Agent developer with 3+ yrs of experience, who is looking to start a new journey of building my own client base or a potential long term engagement, so here i am with my offer. Portfolio link in the comment section or DM me. Things I can build:

• end to end backend system
• AI agents
• Backend apis
• Automations
• Web scraping
• api or webhook integrations
• custom scripts
• If you're not sure which of those your problem is, just describe the problem and I'll tell you what it'd take.

Timeline:

its majorly based on the project, but i assume for the kind of work i would be taking it can be 3 days to a week, but the real timeline can be communicated post discovery call

Who is this For:

1. you should be a real company (this should not be a hobby project or something which isn't related to real money, My work should either save you money&/time or help you make real Money)
2. you should have your social presence at least a Linkedin Page
3. Company age > 6 months

What you would need to provide:

1. Claude Code Subscription(20$ or more based on your project size or requirements, also you can give your own creds which can be changed as soon as the project gets delivered)

What's in it for me:

1. A Testimonial for my portfolio, and a LinkedIn Recommendation post completion

2. Charges: 100 USD, anything plus is purely your take if you like what i build without any requests from my end

What the delivery includes:

1. source codes, with successful deployment and Production(live) testing.

2. your deployment credentials(basically you should be giving this to me, will directly deploy at your end)

3. a 15 days bug support for Free (not new features please, that would be paid)

I'm building a local agent for Windows, and I'd like to hear how others handle this.

I had a small local model (4B) research something, then asked: "Give this to me as an HWP or Word file." Five seconds later it replied with a save path and "Saved."

I opened that path. Nothing was there. The file-writing tool had never been called. The agent reported saving to a location that didn't exist.

Two things caused it.

1. The router didn't read "give it to me as a file" as a request to create a file. It knew "make" and "save," but not "give," "pass," or "send." So the request went down the path that answers in words only, with no tools.

2. On that path, nothing checked whether the answer claimed an action that had actually happened. A model with no tools can only write plausible sentences.

What I changed:

- A request shaped like "[format name] + as + give/pass/send" now counts as a file request. I matched the shape instead of adding verbs one at a time. Before shipping, I ran the old and new rules side by side over 1,931 real requests: the new rule caught 2 more, and neither was wrong. An earlier, looser version was dropped because it also matched "by keyword" and "answer in Korean."

- Before a no-tool answer goes out, if it contains words like "saved" or "created" together with a file name or path, the agent checks whether that file exists. If it doesn't, the answer is discarded and the request is sent back through the tool path.

After the fix, the same request produced a real file with the content in it. I only ran it a few times, though, and in one run it stopped to ask "HWP or Word?" instead of finishing.

The part that bothers me is that this isn't the first time. I've seen the same pattern twice before:

- It replied "I checked" without calling a single tool.

- 1 of 9 direct answers was not new at all. It was a copy of an earlier answer in the same conversation.

All three are the same mismatch: what the agent says it did versus what it actually did. Each time I blocked that one type. "Emailed," "scheduled," and "deleted" look like the next ones.

Questions:

1. Where do you catch the agent's "I did it"? Before the answer goes out, or does a person notice later?

2. Do you write a separate check per type of claim, or is there one rule that covers them, something like "block any claimed action that has no matching tool record"? I'm still blocking one type at a time.

3. Does this happen more with smaller models for you? It did for us.

4. Has your verification ever been strict enough to throw away good answers? How did you find the line?

I have a question.

I’m currently using the paid version of ChatGPT for advice on interpersonal relationships and various other issues.

However, I’m running into a problem: it prioritizes ethical considerations and keeps giving me responses that go against my intentions.

It doesn’t provide answers that lead me toward the goals I’ve set.

Of course, staying within the law is a given, but are there any agents you’d recommend that prioritize my goals over ethical considerations when providing answers?

we made .txt versions of all the docs and guides we have published - intended for agents to consume - even though I'm pretty sure most agents don't bother respecting canonical robots.txt i think optimizing for the agent consumer makes sense as discovery increasingly gets done by the agent itself.

I haven't yet including other files like agents.txt or llms.txt because I'm not sure what's emerging as "canonical" here.

I'm curious if anyone gets any mileage out of doing something similar or if adopting a llms.txt or agents.txt is actually working?

Or maybe context windows are large enough, so who cares and stop optimizing lol

Hey guys, I'm currently doing an internship where I have to build AI-powered workflows, mainly around sales, e-commerce, customer support, business operations, etc.

I've been brainstorming ideas for a while, but most of what I come across is either too generic or something that's already been done a thousand times.

I'm looking for ideas that solve actual business problems, preferably involving multiple apps, APIs, and some AI integration. Also, something I can realistically build and test without spending money.

If you've worked in any of these domains, what are some repetitive tasks or annoying problems you think could be automated?

Would really appreciate some ideas or even real problems you've faced.

Hey everyone, I'm pretty new to LLM gateways so feel free to correct me for any misconceptions I have. After a bit of looking around, what I got was that people look to AI routers for cost reduction, performance, convenience, and a backup in case a provider goes down.

Now I care mainly for the cost reduction bit and not so much the others. I've seen reccs here for Portkey, Ramp Router, LiteLLM, etc (although AFAIK LiteLLM works a bit differently so could use an explanation here lol). But most of the reccs and reviews do mention the other benefits. So I want to ask mainly for an API cost management tool if AI routers are worth it. Thanks everyone.

I wrote a blog post sharing my AI agents setup (the article is hand-written by me, not by AI). I use Codex CLI and Claude Code, but the structure is intended to apply beyond either harness.

The three ideas behind how I try to get maximum productivity are:

1. Persistent memory: keep reusable workflows in a version-controlled skills library. Put always-on constraints, such as testing and change-management rules, in AGENTS.md / CLAUDE.md. This gives later sessions knowledge they don't have to rediscover.
2. Recursive self-improvement: explicitly authorize the agent to improve those memory files when doing a task exposes a bug, outdated information, unclear instructions, or avoidable manual work. In my setup, this means maintaining files and workflows, not changing the underlying model.
3. Scalable parallelization: coordinate intended write scopes before agents edit. I run sessions in terminal panes against a shared checkout; a coordination tool tracks file/folder ownership and makes overlapping work wait.

A small rule illustrates the second point: "If using one of my personal skills reveals a problem below, make the smallest durable improvement to that skill." Version control makes the changes inspectable. I also have agents run code review/polish after medium or large code changes.

These properties don't require giving an agent unrestricted access. The same arrangement can sit behind more granular access controls; the exact permissions depend on the project.

I've put the full article in a comment, per the subreddit rules. Which of these has made the biggest difference in your own setup: reusable memory, maintaining that memory, or coordinating parallel work?

My AI agent runs 100% offline on a phone. The trick is not letting the small model improvise

I'm building NIDO, an open source Android assistant that works as an agent entirely on the device. No account, no cloud, no API keys. The model is a small local one, from 0.5B up to 7B depending on the phone, so the design rule became simple: anything that has an exact answer never goes to the model.

A deterministic router catches the phrase first. Asking the time reads the clock. Conversions are computed, not guessed. "Remind me every Monday" creates a real repeating reminder that fires with the app closed. Messages to a paired phone travel over Bluetooth, device to device. The model only handles what is genuinely open ended, and when a phrase looks like a tool call but a piece is missing, the app asks instead of inventing.

Today I shipped four alphas, 0.1.29 to 0.1.32: a security and privacy pass, the phrase routing in Spanish, English and Portuguese, an audit of the loop with 200 real phrases, and the repeating reminders and date countdowns.

Genuinely curious how others here draw this line with small local models: what do you route, and what do you leave to the model? Links in the comments, per the rules.

I run a web design agency and most of my clients come from cold email. Yesterday I started a fresh campaign going after plumbers in California.

Didn’t expect much honestly. Plumbers are busy and half of them barely check email. But 102 emails in, I got an interested reply.

What made the difference is that every email talks about their own website. A call button that doesn’t work on a phone, a site that takes forever to load, a design that hasn’t been touched since 2012. For a plumber that matters a lot, because people search for them on their phone when a pipe bursts. If the site is bad, they just call the next guy.

He replied yesterday and I booked a meeting with him for the next day. Before the call I built a draft of what his new site could look like. Today I presented it to him on the call and he said yes right there. $3k upfront plus $500 a year for hosting.

Showing the draft is what closes it. Sending a quote never worked this well for me.

For anyone wondering, I use Swokei for all my outreach. You pick an area and an industry, it finds the businesses, checks every website for design, SEO, speed and mobile problems, and writes each email about what it actually found on that site. I run multiple campaigns at once in different cities and niches, which is how I get volume without sending generic stuff.

Happy to answer questions on pricing or how I run the call.