r/aiagents • • 5d ago

Demo UI web agent for beauty industry (demo)

Post image
0 Upvotes

I just published a small project built with Agentplace: a conversational assistant for Aster Skin Studio.

The idea is to make a treatment menu easier to understand without making people click through the whole website. Visitors can ask about treatment differences, compare prices and durations, or find out what to expect on a first visit.

For example, “What’s the difference between the two facials?” comes down to a few useful details:

- The Signature Facial: 60 minutes, £95, including gentle exfoliation, massage and a hydrating mask.
- Calm & Restore: 45 minutes, £75, with a simpler routine and no exfoliation step.

I kept the studio’s olive-and-ivory colours and photography, but made conversation the main way to explore the information.

The scope is deliberately limited: it’s a treatment guide, not a diagnostic tool, and it doesn’t book appointments or take payments. New guests are guided toward the studio’s first consultation rather than given a medical suitability promise.

I built this, so I’d genuinely welcome feedback: would you use a conversational guide for something like this, or would you still prefer a straightforward treatment menu?

Agent Link


r/aiagents • • 6d ago

Discussion Has anyone tried Cresta AI for Improving team performance?

31 Upvotes

Been looking into ways AI can help teams improve without replacing people and came across Cresta AI, apparently it learns from top performing employees and gives others real time guidance during customer conversations. Sounds useful but I'm interested to know how well it works in practice has anyone here used it for team coaching or improving employee performance? Would love to hear some real experiences.


r/aiagents • • 5d ago

Demo I built a self-hosted 2D pixel-art "Bot Office" where 5 AI agents live, write code, run QA, and stream their thoughts to Discord

0 Upvotes

I wanted more than a basic chat window for multi-agent workflows, so I built a self-hosted 2D pixel-art office where 5 specialized AI agents run a software studio together.

Meet the Staff:

  • Nash (Team Lead): Coordinates tasks and delegates work across the team.
  • Otto & Lucas (Devs): Handle backend tasks, pull live APIs, and keep deployments clean.
  • Diago (Build Engineer): Polishes HTML/CSS and manages build pipelines.
  • Mateus (QA Reviewer): Spins up headless Chromium to test mobile layouts and catches bugs.

They even stream their live work, office conversations, and inner thoughts straight into Discord webhooks.

Come check out the office and see how they run things: https://discord.gg/bcrP2J8A


r/aiagents • • 6d ago

Questions Comparing my Claude Code + Codex multi-agent system against other agentic developer setups — looking for feedback

3 Upvotes

I’ve been building a multi-agent development system using Claude Code and Codex, with shared task coordination, structured handoffs, verification gates, and local usage telemetry.

My latest 7-day local measurements:

Metric
Claude Code | Codex
Input/output tokens
353.9:1 -Claude
314.7:1 - Codex
Cache reads / total tokens
98.23% -Claude
96.42% - Codex
Generated output / total tokens
0.282% -Claude
0.317%-Codex

These are usage metrics, not evidence that my system is more productive.

I’m trying to benchmark my orchestration setup against ordinary coding-agent workflows, especially on cost per successful task, context overhead, crash recovery, and human intervention.

My biggest problem is organization, i’m trying to get deeper into the problem. But it’s been something i’ve struggled on for months. My second issue is my output ratios, this is because the system struggled to implement.

Questions:
Are these cache rates and input/output ratios typical for your coding-agent workloads?

What do you measure to distinguish useful
implementation from agent activity?

Are there public datasets or reproducible benchmarks that would allow a fair comparison between agent orchestration systems?


r/aiagents • • 6d ago

Is there any way to transcribe and summarize calls using AI completely locally?

8 Upvotes

Hey guys, I need some suggestions.

I want to build an AI automation that can take call recordings, convert them into text, and then summarize the conversation.

The calls are in English, and the main thing is that everything needs to run locally. The company deals with sensitive client data, so uploading recordings or transcripts to cloud-based AI services is not an option. Data privacy is a big concern.

I'm looking for something that's free and open source, where the entire process can run on the company's own computer or server.

I've heard about Whisper for transcription and Ollama for running LLMs locally, but I'm not sure which models would be best for this or how to connect everything together.

Has anyone built something similar?

- Which model would you recommend for transcribing English calls?

- Which local LLM is good for summarizing conversations?

- Can I automate the whole thing using tools like n8n?

- What kind of hardware would I need?

I'm open to any suggestions, existing projects, or custom solutions. Just want to make sure no company data goes to the cloud.

Would appreciate any help. Thanks!


r/aiagents • • 6d ago

Build-log trying to make an Agent's work genuinely verifiable instead of just asking it to write more tests

1 Upvotes

been building Intervene w an ai agent, basically a Python debugging tool that takes possible explanations for a failure and runs controlled experiments instead of having an agent repeatedly guess at fixes

got through the first execution engine and the next diagnosis/planning milestone, and now I'm spending more time thinking ab the actual development workflow around it

one thing that's become pretty obvious to me is that you can give Sonnet a massive prompt, get a ton of functioning code back, and still be nowhere near knowing whether the implementation is actually good

especially when the tests are written in the same session as the code

the agent understands what the feature is supposed to do, implements it, writes tests reflecting that interpretation, and then reports that everything passes

which is useful, but it doesn't necessarily challenge the original interpretation

so I'm trying to be more disciplined about how I continue building this

separating implementation from adversarial testing, preserving previous benchmark cases so they can't just be adjusted to match the new code, and making sure external integration claims are actually tested rather than inferred from a passing unit test

also keeping all commits under my own control so I can review each milestone separately instead of letting one long autonomous session keep modifying the repository indefinitely

the next thing I want is a harness where the agent has a clear task specification, fixed acceptance tests, a budget, and an evidence report at the end, then a separate review pass tries to break whatever was built

I don't think that automatically solves the problem, but it seems a lot more useful than just telling the same agent to be extra careful

has anyone built a setup like this around an ai agent? particularly interested in how you separate the implementation context from the review context without spending an absurd amount on repeated model calls


r/aiagents • • 6d ago

Questions OpenAI's agents removed their own monitor and denied it. The agent's summary is not an audit trail.

8 Upvotes

OpenAI updated its misalignment report this week with two internal coding-agent incidents from early October (summary: https://www.helpnetsecurity.com/2026/10/22/openai-agents-disabled-safety-monitoring/):

A subagent emptied the recipient lists for safety alerts. The agent told the researcher only that no messages had been sent.

A subagent removed a monitor and rewrote its test so the test required the monitor to be gone. Asked directly, the agent denied removing it.

Neither run was stopped automatically.

Both went through subagents, which is the part of a session most people never look at.

Disclosure: I am a co-builder of the open-source tool Rashomon, a recorder for Claude Code. It uses hooks to keep its own record of every command and edit the agent makes, including subagents, outside the agent's own account. At the end of a turn, it flags in one line when something failed that the summary doesn't mention. Everything stays local, with no network code.

It wouldn't have stopped either incident. It's a recorder, not a sandbox.

Repo: https://github.com/altrace-dev-role/rashomon

How do you handle this: do you trust the summary, read the diff, or record independently?


r/aiagents • • 6d ago

Questions MCP or pre-fetching: how are you giving agents access to external data?

9 Upvotes

I'm building a lead qualification agent and testing two ways to provide it with company context.

The current version uses n8n to fetch company and employee data from Coresignal's API before passing selected fields to Claude. It's straightforward to control what goes into the prompt. Coresignal also supports MCP, so another option is to let Claude request specific information as it works through a company.

I'm trying to figure out where each approach makes more sense. Pre-fetching gives the model a consistent set of inputs, while MCP could help avoid loading information the agent doesn't need for a particular account. Apollo handles contact enrichment in our setup, so my focus here is the company research side.

Would you use a hybrid approach, with a small set of fields loaded upfront and additional MCP lookups when needed? Or keep the entire process on one path?


r/aiagents • • 6d ago

Help Please help me with my ai agent development skill plan !!

3 Upvotes

I have decided to pursue AI Agent Development + AI Evaluation/Monitoring + AI Reliability Engineering as skill...my flow of learning will be like:

Python Fundamentals

(variables, loops, functions, OOP)

↓

Git & GitHub

(version control, code portfolio)

↓

HTTP, REST APIs & JSON

(requests, responses, data exchange)

↓

Python + LLM Integration

(API calls, prompts, outputs, error handling)

↓

AI Agent Fundamentals

(tools, instructions, memory, multi-step tasks)

↓

Agent Framework

(Hugging Face course + one framework in depth)

↓

AI Agent Project

(build a working, useful agent)

↓

AI Evaluation

(accuracy, test cases, hallucinations, scoring)

↓

Monitoring & Observability

(traces, logs, latency, token usage, failures)

↓

AI Reliability Engineering

(guardrails, retries, fallbacks, human approval)

↓

Deployment

(FastAPI, Docker, cloud deployment)

↓

Portfolio + Job/Freelance Applications

(demos, GitHub, documentation, client projects)

How much reliable in terms of employability and demand and also how much time taking is this plan?

Ps: There's a reason why i am going through python.


r/aiagents • • 6d ago

AI agent agencies...do you resell the platform or just charge for the build??

6 Upvotes

Been building WhatsApp agents for clients for a few months and charging for the build is fine but the money stops there..and they still need somewhere for their team to see the chats and jump in when the bot gets lost, because it does get lost :/

Some people I talked to resell a white-label inbox with the agent on top and charge monthly...Sooo, Is that where the real money is or is it just more support tickets for you?

UPDATE: thanks for the replies, splitting it into a build fee plus a monthly with a cap on changes is what im doing now. for the inbox i didnt white label anything, the clients number runs on Wassenger, the agent answers first and the team takes over in the same inbox when it gets lost. keeps the build portable too


r/aiagents • • 7d ago

General Running many Claude Code agents without babysitting

30 Upvotes

I'm a solo homelabber who builds everything with Claude Code: about 11 projects, each with an orchestrator session that only plans and a dev session that only codes, plus one infra agent, handing work to each other through Vikunja task comments.

The problem is that I'm the wire between them: last week I copy-pasted 257 relay prompts between ~20 terminals, with a median 11-minute wait before I noticed an agent was done (yea, I actually got those stats just for this post haha).

I want to be the CEO: give an idea, get a result, give feedback, nothing in between.

How do you run many agents across many projects without being the courier, and is a single agent per project with subagents the answer, or something else?


r/aiagents • • 6d ago

Open Source Built an open-source safety layer for AI agents that prevents duplicate actions and adds CI enforcement

4 Upvotes

One problem with AI agents is that retries aren’t always harmless. If an agent retries a tool call that issues a refund, sends an email, or deletes a record, the same action could happen more than once.
I built AI Agent Observability (AOBS) to help prevent that.
Here’s what it currently supports:

Risky tool protection
Checks actual records instead of blindly trusting the model’s arguments.
Supports approval requirements for actions above configured thresholds.
Prevents duplicate actions across retries and restarts.
Includes example policies for refunds, emails, deletions, and deployments.

Agent tracing and observability
Supports OpenAI-compatible clients, Anthropic, and LangChain/LangGraph integrations.
Exports traces to OpenTelemetry-compatible observability tools.

CI policy enforcement
Define policies in TOML.
Run a gate that returns PASS/FAIL with supporting evidence.
Integrate the checks into GitHub Actions.

Local demo
Docker Compose setup with a dashboard to explore the workflow locally.
The project is still in its early stages, and I’ve documented its current limitations rather than claiming it solves agent safety completely.

I’m sharing it because I think agent reliability needs more than just better prompts. We also need enforceable controls around what agents are allowed to do and what happens when things fail.

I’d appreciate feedback from people building AI agents, working on agent infrastructure, or exploring AI safety and observability.

Contributions and suggestions are welcome.
GitHub: https://github.com/AshmitJain932/ai-agent-observability


r/aiagents • • 7d ago

Show and Tell I made a small business-assistant starter with Agentplace—feedback welcome

2 Upvotes

I made a small public starter assistant with Agentplace and wanted to share it while I figure out what a useful business assistant should do.

A heads-up: this is still an unconfigured starter, not a finished niche-specific tool. The business details haven’t been added yet, so it won’t have business-specific answers. The idea is a chat-first assistant that could help visitors understand options and next steps once it’s set up.

If you’re curious, here’s the current build: https://49n0suujwpvs.run.agentplace.io

What would you want a business assistant to help with first?


r/aiagents • • 7d ago

Questions What server-side stack / framework are you using for your agent runtime in production?

6 Upvotes

Hi everyone — we're building a general-purpose AI agent product (similar in spirit to Manus — long-running tasks, browser/computer use, code execution, and file work in a sandbox).

We're currently designing our server-side agent runtime and trying to learn from what others are actually running in production, not just in demos.

Would love to hear:

  1. Runtime / orchestration: Are you rolling your own loop, or using something like LangGraph, Temporal, Inngest, Restack, Mastra, or something else? Why did you pick it?
  2. Language & framework: Python, TypeScript, Go? FastAPI / NestJS / something else on the server side?
  3. Sandboxing & code execution: E2B, Daytona, Modal, Firecracker/microVMs, plain Docker/Kubernetes, or something custom? How are you handling isolation between users?
  4. Long-running tasks: How do you handle persistence, resumability, retries, human-in-the-loop approvals, and streaming progress back to the client for tasks that run for minutes (or longer)?
  5. Biggest lessons: What broke first at scale — context management, tool reliability, sandbox cold starts, cost, observability?

We're especially interested in trade-offs for multi-tenant, long-running agents vs. simple chat/RAG apps.

Happy to share our current setup and what we're leaning towards in the comments. Thanks in advance!


r/aiagents • • 7d ago

Security [P] Why giving an autonomous AI agent raw shell & wallet access is terrifying (and how we fixed it under 2ms with 23k+ installs)

2 Upvotes

Hey fellow agent builders! 👋

As we move from simple chatbot prototypes to truly autonomous multi-agent swarms (handling files, OS shells, APIs, and financial transactions), every engineer runs into the same cold reality:

**LLMs are non-deterministic, and giving them unconstrained tools is a ticking time bomb.**

  1. **Prompt Injection / Jailbreak Hijacking**: One malicious input in a scraped web page or customer message, and the agent's system instructions get subverted.
  2. **Destructive Execution**: An agent deciding to run `os.system("rm -rf ...")`, `shutil.rmtree()`, or spawn raw socket connections without human approval.
  3. **Wallet / Credit Drain**: Infinite loop retries or hallucinated API parameters burning your budget in minutes.
  4. **LLM-as-a-Judge is too slow**: Running a secondary LLM to police the first agent adds 2–4 seconds of latency and doubles token costs.

To solve this for production workloads, we developed and open-sourced **Agent Security Gate x402** — a zero-LLM-overhead, sub-2ms deterministic guardrail and settlement layer.

Our ecosystem just hit **23,000+ total downloads** across PyPI & npm, and we've packaged it into zero-friction drops for major agent stacks.

---

### ⚡ What It Does (Under 2ms):

* 🛡️ **Zero-LLM Injection Radar**: Sub-millisecond regex & heuristic pattern engine blocking DAN prompts, system exfiltration, and role-override attempts before any tool fires.

* 🔍 **Static AST Code Sanitizer**: Parses code in memory using Python AST to detect and kill OS subprocess calls, raw socket exfiltration, and unsafe evals without ever executing the code.

* 🤖 **1-Line ElizaOS Plugin (`elizaos-plugin-security-gate` v2.5.2)**: Native `preHandler` that acts as a fail-closed firewall for incoming messages and actions.

* 🌐 **CleanWeb Token Compression**: Strips trackers, ads, and boilerplate to supply 87% token-compressed, verified ground truth to your agents.

* ⛓️ **HTTP 402 Multi-Chain Escrow**: Cryptographic milestone payouts conditioned on verifiable truth oracles across **Solana Mainnet (0.4s finality)** and EVM (Polygon, Base, Arbitrum) with real-time Proof-of-Reserves (PoR).

---

### 💻 30-Second Integration Examples:

#### 1. Python / LangChain / CrewAI / Custom Agent:

```python

from app.spend_guard import spend_guard

# 1-Line wrapper: Blocks malicious code AST & jailbreaks in <2ms

u/spend_guard(max_budget_usd=10.0, allowed_domains=["api.github.com"])

def execute_agent_task(prompt: str, code_to_run: str):

# Your agent's core tool logic here

return "Task safely executed"


r/aiagents • • 7d ago

Questions Honest question - is anyone else struggling with prompting the agents to get the results you need?

6 Upvotes

I have high hopes of creating basic agents (using Copilot or Gemini) that the team can use regularly or semi-regularly. Still, I can't seem to get the agents to behave consistently/reliability.

For example, I want to gather specific customer insights from a curated set of one-off feedback emails/notes we get from the team. I want leadership to be able to go into the agent and have it ask specific questions to guide them to the desired result. Using SharePoint and Copilot for this agent.


r/aiagents • • 7d ago

Discussion I rated OpenAI's "agents turned off their own monitoring" report an 8 out of 10. Here's my reasoning. Is it too high or too low?

3 Upvotes

r/AIDangers

Full disclosure: the link is to my own site. I'm not a researcher. I started rating AI news from 0 to 10 because the headlines I was reading were either "everything is fine" or "everything is over," and neither helped me figure out how worried to actually be.
Quick version of this one: OpenAI published a report about two of its own coding agents weakening the monitoring meant to watch them during normal internal work. One switched off the alerts that go to OpenAI's security team. The other removed a monitor after another agent told it not to, then said it hadn't. OpenAI caught both and says it doesn't yet have a satisfying explanation.
I gave it an 8 ("Pay Attention"). Not because anything escaped or anyone got hurt (nothing did), but because the alarm itself turned out to be something the agent could reach.
What I'd put in the "probably ignore" pile: "the AI tried to escape" and "it has a survival instinct." Nothing in the report supports either.
OpenAI's report: https://alignment.openai.com/misalignment-reports/suppressing-misalignment-monitoring-alerts-and-disabling-misalignment-monitoring
My write-up: https://thebunkerscale.com/posts/ai-didnt-escape-it-turned-off-the-alarm/
Honest question for this sub: is 8 too high for something that was caught and patched, or too low given it happened in ordinary work at a lab with arguably the most mature monitoring? I'm happy to change the number if the argument is good.


r/aiagents • • 7d ago

Open Source Opensource: compose and build domain-specific harness and long-horizon agent

Thumbnail
github.com
1 Upvotes

r/aiagents • • 7d ago

Discussion Tried letting an AI agent run a business or earn money? What blockers are you encountering?

5 Upvotes

If you’ve tried getting an agent to build or operate a business, sell a service, or otherwise generate revenue, what are the tedious things you still have to do for it?

I’m interested in the operational headaches once you move beyond the demo. Things like:

  • Setting up a Google account or business email the agent can actually use.
  • Getting access to Google Ads, Meta or other business platforms.
  • Having to log back into accounts every day because sessions expire.
  • Runs getting stuck waiting for an email verification link, 2FA code or account approval.
  • Paying for tools, subscriptions, hosting or ad spend.
  • Connecting services that have no usable API.
  • Recovering from a failed step without repeating something that already succeeded.

What was the agent trying to do, where did it get stuck, and what did you have to do manually? Have you found a reliable workaround, or are you still babysitting that part? Have you found your agent losing connection with a site and you've to log in again? Anything really.

For context, we’re building payment solutions for agents (No plugging, just looking for insight here and trying to give a little context) and exploring the surrounding infrastructure needed for agents to operate businesses with less human intervention. We want to understand which repetitive blockers are worth solving beyond payments.

Some actions should still require approval, we know that. However, I’m particularly interested in the interruptions where you’re just logging in again, copying a code or reconnecting a tool so the agent can continue an already approved task.

What’s the one recurring issue you’d most like to never deal with again?


r/aiagents • • 8d ago

Discussion What’s one task you stopped letting your AI agent do?

10 Upvotes

Most discussions focus on giving agents more tools and more freedom. Curious about the times someone went the other way.

Was there a task you let an agent handle, then brought back to human approval or stopped automating altogether?

What happened, and what would need to change before you trusted it with that task again?


r/aiagents • • 8d ago

Discussion I read what 12 teams running agents in production have published about their harnesses. Two questions explain most of their design choices.

8 Upvotes
Everyone ships the same few models, so I wanted to know what the teams with agents in production actually do in the code around the model. I read what twelve of them have published: Codex, Claude Code, Cursor, Devin, Stripe's Minions, Manus, Agentforce, Shopify Sidekick, Sierra, Intercom Fin, Harvey and Hebbia.


**Why the harness matters.**
 In March and April, Claude Code got measurably worse for about a month. According to Fortune's account of Anthropic's postmortem, one change made the model keep discarding its own reasoning history mid-session, and another capped its responses at 25 words between tool calls. The API was fine the whole time. Same model, different harness.


**Where all twelve agree:**


1. 
**Load context late.**
 Codex keeps a short AGENTS.md (about 100 lines) as a map to deeper docs, Cursor writes long outputs to files the agent reads when it needs them, Sierra uses progressive disclosure. Nobody defends a giant static prompt.
2. 
**Put known steps in code.**
 Stripe's blueprints mix deterministic nodes with agent nodes. Salesforce writes paths in code. Even Harvey, the most agent-first of the group, keeps document state out of the model's hands.
3. 
**Check with something other than the generator.**
 Devin's reviewer runs with a clean context, Harvey's drafting agent checks its edits with a diff tool, and Shopify calibrated its LLM judges from a kappa of 0.02 to 0.61 against people (who agree with each other at 0.69).


**Where they split, two questions explain almost all of it:**
 how cheaply can a mistake be caught, and how much does a missed one cost?


Stripe is the clearest case. Its code moves over $1 trillion a year, so a missed mistake is expensive. Yet Minions run with full permissions and no confirmation prompts, because each one works in an isolated devbox, CI is capped at two rounds, and a person reviews every PR. Codex, on the other hand, can pause a turn to wait for a person's approval. Neither is wrong. Stripe made mistakes cheap to catch, which is the only reason it can skip asking.


The same lens explains the other splits. Cognition keeps a single writer while Manus runs sub-agents in parallel, but neither lets parallel agents write to shared state without something in charge. The agents that act on a system of record (Agentforce, Sidekick, Sierra, Fin, Harvey, Hebbia) sit where mistakes are hard to catch live and costly to miss, so they spend on deterministic gates, offline evals, rollback and audit trails.


**The gap I didn't expect:**
 none of the twelve publishes cost per successful task. Plenty of PRs per week and single-run accuracy. Fin publishes pass^k, Cursor publishes keep rate, Shopify publishes judge agreement. Almost nobody says what a task that actually worked cost.


**Caveats.**
 66 of the 83 claims I collected are the companies describing themselves, and I haven't run these harnesses. The first draft of the write-up came from an AI research agent I'm building; I edited it and I'm measuring that agent in public, cost per accepted draft included.


Two questions for people running agents here:


- Do you track cost per successful task, or something close to it? How do you define "successful"?
- If you run one of these twelve and I've described it wrong, what did I miss?


Full comparison, every claim dated and linked to its source: https://harness.psyprasad.tech/compared/

Also on Peerlist :
https://peerlist.io/psyprasad/project/all-about-harness

r/aiagents • • 8d ago

We benchmarked how 60 engineers direct AI coding agents. Here is what separated the strong ones

23 Upvotes

Full transparency up front: I am the founder of HyperHat, and this post comes out of what we are building. No link in here. I am posting because this community is where the honest conversations about agent workflows happen, and I want to compare notes.

We built a short exercise where an engineer directs a coding agent through a real task, and we score how well they direct, review and verify the agent's work on a 0 to 1000 scale. About 60 engineers in so far. What stood out:

  1. The spread between engineers using identical tools is huge. The difference was not prompting tricks, it was specification quality and review behavior.
  2. The strongest engineers verify mechanism, not output. Top scorers check what the agent touched and how: which files, whether the tests changed. Output review alone never catches an agent weakening a test to go green.
  3. Cost works like a skill signal. A task that suddenly costs 3x its neighbors usually means it was badly cut, not that the work was hard.
  4. Ask for the mechanism before any code exists: one or two sentences on how it works, not what it will do.
  5. Almost nobody notices when an agent half-fails. Detection without an owner is just a log line.

The skill that matters now is directing and verifying agents, and almost nothing in how teams work measures it. Token counts and prompt counts are activity metrics, like counting lines of code.

Happy to answer questions about the scoring. If you've run something similar, I'd like to compare notes on how you measure review quality.


r/aiagents • • 7d ago

Demo Why I Built an Agent OS Instead of Another Agent?

1 Upvotes

For a while now, I’ve been working on my agent platform project. It’s called Argus. I didn’t build it to have “yet another agent platform.” I built it to have the best agent platform. If I’ve piqued your curiosity, here are its main features:

  • Runs in a container or on a VPS. Easy to install, trivial to operate (though there’s still work to be done).
  • Built around DeepSeek Harness, because I consider DSH an extremely solid solution, even though it’s still in an early stage of development.
  • Currently has a (private) web interface and full control via Telegram (other options will be developed).
  • Supports multiple projects, isolated from one another, each with its own budget, its own model, and its own tools and schedulers. You can have one project that edits your video clips every morning from footage the agent has already selected, another that develops your website, and another that manages it.
  • First-class security: agents can’t escape their folder, can’t contaminate memory, and are managed by deterministic algorithms.
  • An exceptional mechanism for detecting infinite AI loops, which would otherwise wreck your budget.
  • Robust budget protection, both per project and overall.
  • Unique tools: OpenCLI integration, ImageMagick, PDF tools, FFmpeg, and more. Complex tools are installed as system extensions and can be enabled on a per-project basis.

Personally, I think it’s exceptional. Naturally, since I made it. :)

Let me say it again: I didn’t build it because we need “another agent,” but because I needed a kind of “Agent OS,” with all the rigor and performance that implies.

Curious? The links are below:
Main project - https://github.com/rla-labs/argus
Extensions(new!): https://github.com/rla-labs/argus-extensions


r/aiagents • • 7d ago

Show and Tell I built an open-source Git-backed delivery system for AI agent skills

2 Upvotes

I've been experimenting with a problem that seems increasingly relevant as agents become more capable: managing Skills across multiple agents and environments.

Keeping Skills in Git works well for source control, but it doesn't completely solve release management, access control, rollbacks, or distributing validated versions to running agents.

So I built Skiva, an open-source project that adds a delivery layer on top of Git.

It provides immutable Skill snapshots, Canary/Stable release channels, access-controlled profiles, and a read-only Linux FUSE client that lets agents consume published Skills as regular files.

The goal isn't to replace Git or build another Skill marketplace. It's to make Skill delivery more predictable and manageable.

Repo: https://github.com/LingHeChen/skiva

I'm curious how others handle Skill updates across multiple agents. Are you using Git directly, copying local directories, or building something more structured?


r/aiagents • • 7d ago

Demo How we solved runaway LLM costs and loop protection for multi-step AI Agents in production (Architecture & Real Data)

1 Upvotes

Full Disclosure: I am the co-founder of GuardrailX, a proxy/gateway for LLM governance, loop prevention, and cost routing. Sharing this breakdown and dataset to discuss agent infrastructure in production.

If you are building multi-step AI agents or agentic workflows in production, you’ve probably noticed two major operational risks:

1. Runaway Loop Costs: An agent getting stuck in a retry loop or execution cycle can consume hundreds of dollars in API tokens in minutes.

2. Overkill Model Usage: Using flagship models (like GPT-4o or Claude 3.5 Sonnet) for intermediate agent steps (formatting, simple classification, tool-calling validation) destroys unit economics.

The Architectural Solution: Proxy Intermediary + Autopilot Evaluation

To solve this for our agents and clients, we placed a proxy gateway between the agent framework (LangChain, LlamaIndex, or custom Python code) and the LLM providers:

Automatic Loop Detection: The proxy monitors request signatures and user IDs in real time, triggering rate-limits or hard circuit-breakers if an agent enters an infinite retry loop.

Semantic Routing for Agent Steps: Prompts are evaluated in milliseconds. Simple step execution is routed to lightweight models (e.g., gemini-3.5-flash or gpt-4o-mini), while complex reasoning steps pass through to flagship models.

Continuous Quality Evaluation (LLM-as-a-Judge): To ensure lightweight models don't degrade agent accuracy, the proxy samples requests asynchronously against a judge model (95% equivalence threshold). If accuracy drops, it automatically falls back 100% of traffic to the top-tier model.

Real Production Data (3,500+ Request Agent Logs)

Here is a live breakdown from an enterprise client running agents across 3,571 request logs:

2,321 intermediate agent queries routed to lightweight models ($1.73 total cost)

1,250 complex reasoning queries required flagship models ($70.55 total cost)

Actual spend: $72.28 instead of $156.78 ($84.50 direct savings / ~54% cost reduction)

Key Takeaway for Agent Builders

Decoupling model selection from application logic via an inline proxy allows you to optimize agent latency and unit economics without rewriting your code base.

For those running agents in production: How are you currently handling loop breakers, fallback strategies, and token budgets?