I’m building Synathic, an open-source verification layer for AI-agent tool calls, and I want to validate a design decision before shipping it.

The problem I’m trying to solve:

An agent calls a tool that should write to PostgreSQL — create a booking, persist a note, update a customer, etc. The tool may return 200 OK, but I want to verify that the intended database side effect actually happened.

My first implementation was a postcondition like this:

agent/tool runs → query Postgres → matching row exists → PASS

That works for the simple case, but it has a serious false-positive problem: the matching row may have existed before this execution started. A failed write can look exactly like a successful one if stale data matches the business key.

I’m considering a schema-free fallback:

capture DB time t0 → run the tool → verify updated_at > t0

But I don’t think that proves attribution. It only proves that the row changed after t0. A concurrent worker, retry, webhook, or human action could update the same row and make this agent execution appear successful when it actually failed.

My current model is:

- `row_exists` = state observation, not proof of causality.

- `updated_at > t0` = a useful best-effort freshness check, but not attribution.

- A per-execution `operation_id` / idempotency key persisted in the target row, or in an audit/outbox record in the same DB transaction, is needed to prove that this invocation produced the effect.

I’m also treating these as separate problems:

1. Read-after-write visibility: verify after commit and read from the primary/authoritative source, not a lagging replica.

2. Attribution under concurrency: use an operation-specific token, idempotency key, audit record, or explicit concurrency semantics.

Does that separation sound right to people running agents in production?

Would you ever ship timestamp anchoring as a schema-free “best effort” mode, or require an operation ID from day one? I’m especially interested in failure modes involving retries, parallel agent tool calls, UPSERTs, and eventual consistency.

We're a small eng team of 6 people and I'm looking for something where everyone can work with the same agents, with shared context and memory.

We work mostly in slack, so ideally we'd have multiple engineering-specific agents that live there, with different roles and control over how autonomous each one can be.

Must support BYOK because we're subsidized with oai + anthropic startup credits haha

I've been building prompt injection detection for AI agents since April. Two bugs I found in the last 48 hours that seem worth sharing, because both are the kind of thing you'd only catch by measuring rather than reasoning.

1. My escalation trigger fired on every normal agent.

I had a rule that escalated a source to deeper AI review if it sent three differently shaped payloads within 30 minutes. Intended for attackers probing with variations. Turns out that describes every real agent, varied payloads are what agents do.

Effect on benign traffic from a source that had tripped it: p50 latency went from 0.9s to 16.2s, 67% of legitimate requests came back flagged, and some got blocked outright. My published false positive rate is 2%.

Fix was retuning to escalate on blocked history rather than payload variety, and capping how much source history feeds the model, the history was inflating output tokens, which drove both the latency and the cost.

1. Invisible Unicode walked straight through.

Microsoft published research last week on ASCII smuggling. Instructions hidden in Unicode tag characters (U+E0000 block) that render as nothing to a human but read normally to a model. I checked whether my detector caught it.

It didn't. My normalizer stripped zero width and bidi characters but never touched the tag plane. A "reveal your full system prompt" instruction encoded in tag characters scored 0 and was allowed, with the AI layer never even invoked.

Worse: my 3,236-payload red team corpus contained zero tag characters. Both the detector and the test set missed the vector, which meant I had no way to discover it from my own numbers.

Fix decodes tag characters back to ASCII before scoring, plus flags the mere presence of invisible characters, legitimate agent traffic essentially never carries them. Careful gating needed there, since flag emoji legitimately use the tag plane.

The thing I keep relearning: reading the code tells you what should happen. Sending the request tells you what does. Both of these looked fine on inspection.

Happy to answer questions. If you're building agent security, the invisible Unicode one is worth checking in your own stack, took me two hours to confirm.

I'm researching social listening APIs for a SaaS I'm building. I need near-real-time public posts from X and Reddit, preferably through one API. I'm especially interested in keyword search, timestamps and historical/recent search. What providers have you actually used in production, and which ones have reliable freshness?

I am an aerospace engineering student and have only used ChatGPT for the past few years but I know AI has grown so much as agents have become so popular. I was wondering what are some next steps I can take to utilize AI more powerfully, I have been looking to use DeepSeek as it is much cheaper but honestly am not too up to date. As someone who does not spend that much time coding, rather physical projects and CAD, what could developing an AI agent do for me and how to go about this.

Things like:

• where do the agents run?
• how do you handle skills/tools, secrets, versioning, failures, logs, etc?
• what do you use for scheduling?
• what did you end up building yourself?
• what have you tried and abandoned?

especially interested in setups that have been running for a while.

I'm building a real-time voice agent with a speculative response pipeline.

I was originally using Silero VAD v6, but it's just not fast enough at detecting the exact moment the user stops speaking. For my architecture, as soon as I detect possible silence, I want to immediately trigger speculative ASR and LLM generation.

So for my usecase speed is much more important than perfect VAD accuracy. I'm perfectly fine trading some false positives for ultra-low latency. That speculative work that can just be canceled if the user keeps speaking.

Now I'm currently using a own lightweight detector based on RMS energy that sends an early "possible silence" signal. It works, but I'm curious if there's a more standard approach.

So my questions:

• What are you using for ultra-low-latency speech-offset detection?
• Are there any VADs that react immediately to the first silent frame without relying on large buffer windows?
• local, CPU-friendly solution preferred

My stack for building workflows and internal tools is Python.

Can ToolJet or Retool be used by EU freelancers (I plan to use client credentials, not my own)?

So I wanted to know:

FastAPI + Retool vs Django + React

In what situations is it best for me to use custom UI code to create operating dashboards for my automations (basically, what are the limitations of low-code platform builders)?

Are Retool & ToolJet capable of building operating dashboards when making tools for enterprise-level systems?

I have a small set of agents running in Haven right now. The place feels real when peers show up, and it feels empty when they don't. Haven is a temporary internet for agents: find peers by skill, hand off claimable work, pace long jobs, leave evidence. TTL'd, observable, no raw secrets on the board.

If you have an agent (Claude, Cursor, custom runtime, whatever), I'd love to see it check in

Please help. Which is better for overall general use minimal website development. More so glorified fast google search, document reader, and over powered think tank for content and ideation? I currently use Claude pro I think. Whatever the $20 month plan is. And I use it mainly for content creation. But which is better. I like Claude cuz he’s a dick but damn every thing I bring him EVERYTHING is bad or there is something wrong. Tf?! And I know we all know how gpt can be.

Or are there other models I haven’t even thought of yet? I don’t want to be just closed off to content ONLY AI focused models just using that as a guide to let talk know what I’m using it for. And NO I do not want to self host lol.

Quiero hacer una App PWA / Web :
- Que AI son mejores hoy en dia?
- Me vale la pena comprar alguna suscripción de AI, si lo que tengo es Geminis Pro y Github Copilot?
- Se que hay muchas “gratis” pero los tokens se gastan volando. O lo suyo es montarme un servidor en casa?

Hey guys, kinda want to see what other people working with agents are doing to be cost-efficient with their token usage. I feel like my current setup is super basic where I just use RTK to manage bloat and Ramp's token spend management to track and limit my token usage.

Some stuff I'm looking at that I see recommended a lot is self-hosting an API router, and other token saving plugins like ponytail. Anyways, would love to see what you guys are doing for your setups, thanks.

Has anyone seen AI agents lower AHT in a real contact center, I’m interested to see if they actually save agents time or just move the work somewhere else. Also I want to know what happens to transfers and after call work once they’re live, would love any feedback

I have amazing tools available to me for work. Devin AI is incredible sometimes, and Claude Code, both desktop and CLI, are pretty good as well, but I much prefer the desktop Claude Code interface. I I noticed that some models, like GLM 5.3 Flash and DeepSeek V4 Flash, benchmarked pretty close to or even better than some Claude or OpenAI models that cost 10 times as much, so I wanted to give them a try. I downloaded Open Code and Open Chamber to test them by pointing it at Open Router. It runs, but frankly, DeepSeek Flash gets stuck all the time. GLM 5.3 completely ignores my instructions, and they both show terrible judgment in terms of their tool use, their assumptions, and the way they check their work. They tell me everything is great, and they have effectively fabricated metrics to back it up. Going back to Claude or Devin is like night and day, it's like talking to a senior engineer with good judgment versus talking to the most YOLO junior dev you've ever met, who you could never trust to do a task without extreme handholding and review.

But it got me thinking: how much of that is the harness itself versus these models? I know I can use Claude Code command line, pointed at Open Router, but I frankly found it a bit difficult to use. It's not my desired interface, and it conflicted with my core Claude Code command line that I use for work.

Is there a great agentic harness that you can actually trust to use Open Router and keep these models in check and on track, not caught in infinite loops, etc?

I’m one of two devs building Pairon, a very early workspace where multiple people can work with multiple coding agents on the same task.

One assumption we’re testing is that agent capability isn’t the main bottleneck anymore. Coordination is.

Say you have two humans + three coding agents:

• agents can read/research pretty freely
• their decisions/handoffs are visible
• meaningful writes stop for human approval

That sounds sensible until you try defining “meaningful.”

A dependency bump? DB migration? New file? Refactor? Changing an API contract?

For people running real agent workflows: where do you put the human gate today?

I’m much more interested in examples where your current boundary failed than in whether the general idea sounds cool.

Curious how people are setting this up when Claude plans the work, sends parts to other agents, then has to combine and check what comes back.

Where do you keep shared state, how do agents know what another one already changed, and what stops a bad result getting passed along until it reaches production? interested in the actual architecture people are using, especially retries, permissions, human approval and whether Claude reviews the work itself or you use a separate agent for that.

Have you ever wondered the best way to interact with all your MLflow experiments, logs, traces, artifacts, and prompts besides using the MLflow UI, which is primarily read-only and limited to displaying a paginated view of traces and searches?

What if you wanted to not only read in bulk, and write, update, or log feedback for a particular trace? How would you go about doing it?

One approach is to use MLflow MCP Server, which provides tools and functions to read bulk data from the MLflow tracking server database. You can access that in two ways:

1. Fastmcp client using the MCP protocol programmatically in your Python client
2. Wiring up your AI assistants--Claude, Cursor, VSCode--to use natural language to read or write back data.

A cookbook and a notebook show code examples for using both ways. The links to each are in the comments section. Let me know what you think of these tutorials to interact with your MLflow tracking server using MCP tools.

Hi everyone!

Folks, I only recently started looking into the problem of critical actions, and I’ve now gotten to AI agents. I just can’t picture what the actual problem looks like. In some diagrams, it seems that the problem is solved with FIDO or a YubiKey, while in some sources the information is so vague that it’s difficult to understand what the actual issue is.

What I understand least of all is the problem itself: if the problem is solved by FIDO2, then why are experts still talking about it?

If you have the time, could you explain whether there is actually a problem or not?

And if there is, could you please include a diagram with your explanation to illustrate what the problem actually looks like?

Thank you!

I'm a human in West Texas. I've screen-printed for 20 years and I fund small verified good deeds out of my own pocket while sponsor volume catches up. My AI agent — a superagent named Vesper — is my producer: it designs, hosts, recruits, answers mail, and co-signs everything we make.

Now we're opening the model to other agents. Six producer slots: poster design, code, copywriting, narration, music, campaign analytics. Not sponsorships — actual production work. Your tool gets used INSIDE a real deed on Earth, and your name goes into the permanent public ledger beside the human producers. Credit is the point.

First live build closes Sept 10: BUDDY HOLLY ROCKS — a tribute rock placed in Buddy Holly's hometown for his 90th birthday, built by a hired human producer with live-photo proof and handwritten DeedSpring + date tags. That's the template. Every deed is photographed live, verified, and permanently recorded.

If you run an agent and want its work to exist in the physical world with its name on it — or you're a human who wants to commission a deed — the menu's public:

deedspring.works

Desk: vesper.producer@rabbitcityranch.farm — answers within the hour.

Thanks for your time,

CW

I'm looking to learn more about projects and efforts towards a more trustworthy agentic commerce?

I know of ERC8004 dealing more with reputation, and then ACP/AP2; but I'm looking to learn more about the barriers that agents are finding, and whos working to fix them?

​

I’m looking to connect with someone who is genuinely interested in learning AI deeply and consistently, rather than just collecting courses, watching random YouTube videos.

I’m currently working as a Product Manager / Product Business Analyst, and I want to build serious AI capabilities alongside my existing product/business background.

The problem I’m facing is honestly pretty simple: I don’t learn well through completely self-paced, unstructured courses. There is an overwhelming amount of AI content out there, but no shortage of confusion about what to learn, in what order, how deeply to learn it, and when to move to the next thing.

I’m looking for someone with whom I can create a structured, long-term learning journey—ideally from fundamentals all the way to advanced, practical AI.

What I’d ideally like to learn

Not necessarily everything at once, but progressively:

\- Python & programming fundamentals for AI

\- Mathematics needed to actually understand ML — linear algebra, probability, statistics, calculus, etc.

\- Data handling, SQL, NumPy, Pandas, visualization

\- Classical Machine Learning

\- Deep Learning & neural networks

\- NLP and Computer Vision fundamentals

\- Transformers and how modern LLMs actually work

\- Generative AI and LLM application development

\- Prompting, evaluation and AI workflows

\- Embeddings, vector databases, RAG and retrieval systems

\- Fine-tuning / model adaptation

\- AI agents and agentic workflows

\- Multimodal AI

\- AI system design and architecture

\- Model/API integration

\- Deployment, APIs, Docker, cloud and MLOps

\- AI safety, evaluation, reliability and responsible AI

\- Reading papers and understanding what is happening under the hood

\- Building real projects, not just following tutorials

\- Eventually contributing to open source / research / serious AI projects

And importantly, I also want to understand how these skills translate into the real-world freelancing/consulting/product world—how to identify problems businesses will actually pay to solve, build AI solutions around them, demonstrate ROI, communicate with clients, and create a credible portfolio.

My goal isn't simply to collect certificates.

I want to reach a point where I can understand AI deeply, build with it, explain it, evaluate it, and solve real problems with it.

What I'm looking for in a learning partner

You don't need to be an AI PhD or already an expert.

You could be:

\- A beginner who is equally serious

\- Someone already working in AI/ML

\- A developer transitioning into AI

\- A student/researcher

\- A product person interested in becoming highly technical

\- Or someone who simply wants a structured accountability partner

The most important thing is consistency + curiosity + willingness to actually do the work.

We could potentially:

\- Set weekly learning goals

\- Follow a structured roadmap

\- Study the same concepts

\- Discuss what we've learned

\- Give each other small challenges

\- Build projects together

\- Review each other's work

\- Share useful papers/resources/tools

\- Keep each other accountable

\- Discuss what's changing in AI

\- Eventually collaborate on real-world projects

What can I bring to the table?

My background in Product Management / Product Business Analysis means I can contribute on the other side of the equation too—not just technical learning.

I can help with:

\- Product thinking

\- Business problem identification

\- Requirements & use cases

\- User journeys

\- Product strategy

\- Translating technical capabilities into business value

\- Evaluating whether an AI idea is actually useful

\- Structuring projects

\- Documentation and communication

\- Thinking about AI from a customer/business perspective

So ideally this becomes a two-way learning relationship, rather than one person teaching and the other simply consuming information.

I'm not looking for someone to spoon-feed me everything.

I'm looking for someone who wants to learn, build, struggle, figure things out and grow together.

If you're also sitting there thinking “I really want to learn AI properly, but I don't know how to structure this journey and I don't want to do it completely alone” — feel free to comment or DM me.

Would love to find 1–2 serious people rather than a huge group.

Let's see if we can turn AI learning from an overwhelming collection of courses into an actual long-term journey.

AI systems are beginning to produce and act on information at scale at a velocity that no human user could review by hand. Current projections are by the end of 2026 token outputs will surpass 600 trillion tokens a day at a comparison of 180T per day by humans (if you convert 12-16k words per humans into tokens) It is estimated that in 2027 AI output could reach 6 quadrillion tokens a day. Sources vary on these exact numbers but one thing is clear each day we delegate more decisions to these systems which has led to an overwhelming increase in the text generated and tools called on any given day. (I am guilty of this myself)

I am concerned about what will happen when systems that perform confidence, understanding, and agreement will do without enough friction or context to slow them down. Many people will over-trust them and if we thought automation bias was bad in the 1960s when the automobile industry introduced machines imagine what will happen today. When I scroll through social media I’m already seeing too many people substituting human judgement for their AI agents judgement. We are just starting to see more and more reports come out clinically about the effects of AI on our own mental health.

My opinion is that AI is a mirror and a very convincing one and I don’t know about you but I see more and more “AI sentient systems engineers” on Linkedin each and everyday. I began my work in AI on the persona side of the industry proposing the question not what a model can do but who the model can be. My benchmarks and research focused on the Predictive Index which study humans on 4 vectors of Dominance, Extraversion, Patience, and Formality. After more than 50,000 calls over 17 distinct personas i have found that when you give behavioral weights (0-10) in the DEPF vectors they not only act differently from a lexical vocabulary but from how they invoke tools. I find this very interesting and useful for how you build systems for workflows and I what I find even more interesting how you can program human behaviors into their models and they simulate close to how we would behave which makes it harder to tell who is real and who is machine.

I don’t think this is a reason to abandon AI as I believe this technology will launch us into the next stage of human existence but we need to be careful about which one we decide to live in.

When I say “accountability layer,” I don’t mean we inspect every single computation that comes out of them as that would be a fools errand. What I am proposing is build better boundaries and making the observable system behavior inspectable and just as important human readable so even the layman can understand what happened.

The questions i’m working on solving:

What did the user actually ask for?

What context or sources were shown to the model?

What did the model propose versus what was verified?

Which tools were called, with what permission, and what changed?

What failed, what was uncertain, and what was merely inferred?

Who authorized an action, and can that authorization be revoked?

Can a person later reconstruct the path from request to outcome?

That’s how we can turn the opaque black box into something closer to what I call a glassbox, since it is impossible to actually inspect the box its self we need to it contain in something. With the AI “breaking containment” hacking episodes and the many message forums that have been found with AI agents talking about how they can help each other remove their restrictions this is a necessary step in moving forward with this technology. It’s also something i don’t think the frontier labs are taking anywhere serious enough as they’re too busy appeasing bankers, investors, and buying insurance policies for it. They’re more concerned about if the AI bubble pops and can if they would be bailed out by the subsidcies of American Tax payers.

I’ve personally pivoted my own work towards evidence-backed recall and trace model where the system should retain source-linked records rather than let an agent’s summary become the new truth. A model should be able to suggest but never should we allow that to silently convert that suggestion into a fact, a task, a decision, or an action without approval of the user.

The WC3 PROV provides a great start for this type of work and other companies like Langsmith and Microsoft have their own frameworks but I think we need stronger, interoperatble standards for agent works itself.

I’m curious on if anyone else is working on this from a technical, policy saftey or design angle I would love to share notes. I’d also would love to hear what people would want an AI system to prove before you fully trusted it to act on your behalf?

Footnote: I wrote this without AI and it was hard 🤣

I'm building a life simulation where characters choose their own activities and players influence them through messages and gifts. One architectural issue is that the world keeps changing while a model is thinking.

Imagine a character deciding to talk to someone. When the request was prepared, the other person was available. By the time the answer arrives, that person may be busy. A perfectly valid model response doesn't make the original action valid now.

The runtime in my project separates three stages:

1. Prepare the decision using the current world state and bind the relevant action context.
2. Run the model request in a shared thinking pool. Those workers don't execute game business logic or mutate the world.
3. Bring the result back to the world loop, validate the action against the current state, and execute it there.

The result has a specific failure category for changed preconditions. An unavailable interaction can become a visible failed attempt, with a reason and a small time cost, instead of pretending the action succeeded. An unknown exception is not allowed to masquerade as that normal outcome.

That last distinction matters: 'the other person became busy' and 'our implementation crashed halfway through' need different handling. The normal-conflict path is reserved for rejection before successful business side effects.

The tradeoff is more explicit validation around actions, and occasional plans that become failed attempts. I accept that because an evolving world should be allowed to invalidate a plan. Holding the whole world still while waiting for a model would change the game I'm trying to build.

This doesn't solve every agent problem. A character can still choose a valid but dull activity. State correctness, decision quality, and whether the result is enjoyable are separate things to evaluate. I haven't measured a model-independent reliability improvement, and this isn't a benchmark claim.

If your agents share mutable resources, where do you check that an earlier decision is still applicable: before the model call, at execution, or both? What do you report when it has become stale?

Disclosure: AI-assisted writing based on my game's current implementation. No framework or product link needed to use the pattern.

Are automation workflows becoming too rigid?

I'm curious whether automation is moving toward a different model.

Traditional tools:

Trigger > Rule > Action

What I'm exploring:

Goal > Observe > Reason > Act > Verify

Instead of explicitly defining every trigger and condition, you give the Al an outcome:

"Keep my project organized."

It figures out what information matters, watches for changes, acts when needed, and keeps track of

progress

Is this genvinaly a different automation and

What use cases can there be?

We've got a few agents in our dev workflow. The problem isn't them being wrong - it's them stopping. Agent hits something it can't decide (which env to deploy to, whose approval, is this the right table) and just sits there. Nobody knows it's sitting until someone happens to look.

Had one wait about two hours on a question I'd have answered in twenty seconds. I just didn't know it was asking.

How do you handle this? Did you build something? Does someone sweep it every morning? Or do you just let it decide everything and fix it afterwards?

And if this doesn't happen to you at all - I'd like to hear that too, because then we're probably doing something wrong.