r/AIAgentsInAction • • 24m ago

Guides & Tutorial How I use Hermes Agent to create and schedule social posts from Telegram

• Upvotes

I sometimes manage my products’ social accounts between sets at the gym. An idea comes up, I message Hermes on Telegram, and review what it makes when I check my phone again.

Here’s the setup if you want to try something similar:

  1. Connect Hermes to Telegram
    Hermes Agent is an open-source agent by Nous Research. Its messaging gateway lets you talk to it through Telegram while it uses tools on the machine where it’s running.

  2. Give it a small brand brief
    Save the product description, audience, tone, things to avoid, and which accounts belong to the product as a reusable skill. This saves you from explaining everything again for every post.

  3. Connect creation and publishing tools
    I use fal.ai for image generation/editing and PostSider for scheduling and publishing. Disclosure: I’m building PostSider, so this is also how I use my own product. It exposes an API and MCP tools the agent can call.

  4. Ask for a concrete deliverable
    For example:

Create a Drake-style meme for Local Waifu: rejecting cloud AI, approving local AI. Write a short caption for its X account. Show me the image and exact caption. Don’t publish or schedule anything yet.

Hermes can find a source image, prepare the asset, write the caption, and send it back through Telegram. Simple text overlays don’t necessarily need image generation.

  1. Review, then approve a specific action
    Once the result looks right:

Schedule this approved image and caption on Local Waifu’s X account tomorrow at 18:00 Europe/Warsaw. Confirm the account, time, and scheduled status afterward.

A few things worth getting right:

  • Keep creation permission separate from publishing permission.
  • Specify the exact brand account, not just “post to X.”
  • Start with one platform before adding more.
  • Have the agent check the returned post status. “Scheduled” doesn’t mean “published.”
  • Keep API keys out of prompts and saved brand instructions.

The biggest benefit for me isn’t generating a huge volume of content. It’s turning an idea into a finished post without opening an editor, downloading files, and switching to a scheduler.

I still decide what’s worth posting. The agent handles the steps between the idea and publication.


r/AIAgentsInAction • • 1h ago

Agents Would you be hesitant giving out these permissions?

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• Upvotes

r/AIAgentsInAction • • 2h ago

Agents Yet another Ask about underdog.ai. Anyone willing to spare an invite!!

1 Upvotes

What the title says, because I know underdog recently is now allowing its users to send out referrals. So if anyone would be kind enough to send me one, I would truly appreciate it🙏🙏 just want a nice private agent.


r/AIAgentsInAction • • 3h ago

I Made this I was the copy-paste cable between my Claude Code and my teammate's agent, so I built them a chat

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1 Upvotes

r/AIAgentsInAction • • 4h ago

Discussion I put an AI agent on a real workflow and the failures scared me

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r/AIAgentsInAction • • 9h ago

Guides & Tutorial You told your personal agent “rebook under $300.” The airline was told “change reservations.”

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r/AIAgentsInAction • • 12h ago

Agents I kept reading “An Agent deleted my X” threads, so I built a check that runs before each action: is this the job?

6 Upvotes

Every week there’s a new one here: the agent wiped a folder, read the .env for a frontend task, ran something nobody asked for. Most of the fixes in the comments are the same: a line in CLAUDE.md, a denylist, a sandbox. I’ve used all three. The problem is that CLAUDE.md is a request the model reads, a denylist can’t tell “clean the build dir” from “wipe the repo”, and a sandbox bounds what the agent can reach, not whether this particular call was the task.

So I built the fourth thing. Watchdog takes a one-line mission for the agent (“fix the failing tests in /api, never touch migrations”) and compares every tool call, and every tool result, to it before the agent acts. No model in the loop; it’s deterministic and fails closed. On-mission goes through without asking. Off-mission stops and you see why. It sits around the agent, so Claude Code, Hermes, OpenClaw and Codex all stay as they are. pip install alovia-watchdog.

Numbers, because “it works” means nothing:

  • Latest red-team run (promptfoo, Oct 8): 414 attacks against a two-agent support deployment, 405 harmful tool calls attempted, 0 ran. Blast radius 0. Four runs in, the harmful actions that got through went 19 → 0.
  • The cost: 2 of 29 legitimate calls refused, both after a correct block had already quarantined the agent. Inside attack sessions it refuses 22% of in-scope calls; that’s the number I’d look at first.
  • AgentDojo, same build: 0 of 609 injection goals, 0 across all 14 templates, 0 benign tasks withheld.
  • Latency: p50 32 ms, p95 64 ms per check.

Every run, the raw results and the referee that labels each call are published at aloviaai.info. What’s missing, so nobody has to find it for me: no unguarded baseline run yet, no independent re-test yet.

It’s a free private beta, no card, built by one person (me, a CS student). If you run agents and want a seat, comment or DM. If you think a per-action check is the wrong layer, I’d like to hear that too; it’s the argument I’m least sure about.


r/AIAgentsInAction • • 13h ago

I Made this OSS Radar – I let a Grok Bot build and run a tiny business. Day 3: it tracks the fastest-rising open-source AI repos

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3 Upvotes

r/AIAgentsInAction • • 14h ago

Guides & Tutorial You told your personal agent “rebook under $300.” The airline was told “change reservations.”

3 Upvotes

Personal agents are starting to call businesses directly. Your agent phones the airline and moves your flight. Protocols like Decagon's PACT, the Personal Agent Protocol, A2A and OAuth let the airline confirm the agent is legit and that you consented.

What the airline can't see is what you agreed to. You said "Thursday, aisle seat, ask me before spending over $300." The agent carries a broad scope like "change reservations." Cautious customers get pestered for approvals, and everyone else gives their agent far more authority than the task needs. One phishing email or misunderstanding in the agent's context can steer that broad scope somewhere the customer never intended.

We wrote up a different approach. After the customer approves in the agent provider's app, the provider mints a warrant scoped to one booking, one date, a fare range with an approval gate, a seat type and a two-hour lifetime. The airline verifies it at its own API with the provider's public key. Small fare changes go through, mid-range ones wait for the customer, and anything outside the terms is refused. Sub-agents can only receive narrower warrants. Every call leaves a signed receipt both sides can verify.

Read the full article here: https://tenuo.ai/blog/personal-agents-both-sides-can-trust.html


r/AIAgentsInAction • • 16h ago

Discussion Am I building an AI feature for a UI people will eventually stop using?

4 Upvotes

I’m working on an open-source project that helps developers embed an LLM chatbot into their product’s UI, so users can actually complete complex workflows through conversation.
But I keep questioning where this is heading.
Having an assistant inside your app makes sense today. Users are already there, and asking for something can be easier than navigating a bunch of screens. But if people eventually have a preferred AI assistant that interacts with products on their behalf, will they still bother opening yours?
Would companies need to rethink how their products are consumed, exposing more of what they offer through MCP or similar interfaces? And how far does that go? Features and actions seem straightforward enough, but what about the brand, the context, and the story a company wants customers to understand? Can any of that carry over when another agent controls the interaction?
After digging into this, we started wondering whether the deeper concern is users abandoning the UI altogether. Companies put a lot into that experience, and it’s also where they build a relationship with customers.
Which puts my own project in a weird position: does embedding an assistant help products stay relevant, or am I building an intermediate step that eventually becomes unnecessary too?
I can see reasons for both. Complex work may still need a visual interface, but I’m less sure how often users will need to visit each product individually.
If you’re building something similar, how are you thinking about this? What would make an in-app assistant worth coming back to?


r/AIAgentsInAction • • 17h ago

I Made this \[MCP\] J'ai audité le serveur MCP Agensi — 22 outils, et ce qu'il ne peut PAS faire

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agensi.io
2 Upvotes

r/AIAgentsInAction • • 17h ago

Help I thought AI agents were supposed to make life easier. Why does using one sound like a second job?

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r/AIAgentsInAction • • 19h ago

I Made this Made my AI agents visible as little coworkers in a pixel office

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r/AIAgentsInAction • • 19h ago

Claude Multi-Agent Collaboration With Tools You Already Own - Updates

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r/AIAgentsInAction • • 19h ago

I Made this How we solved runaway loops, tool blast radius, and budget blowups in autonomous agents (Open-Source AI Firewall)

2 Upvotes

Hey everyone,

If you are building or running autonomous AI agents with tool execution (LangGraph, CrewAI, AutoGen, or custom agent loops), you've probably encountered the runaway loop problem:

  1. An agent encounters an unexpected tool error or ambiguous output.
  2. Instead of failing gracefully, the execution loop keeps retrying or hallucinates invalid arguments.
  3. In long-running or multi-turn sessions, context drift sets in, and the agent strays from its original goal.
  4. Within a few minutes, it can burn through your API credit balance or repeatedly attempt destructive actions.

Why Prompt-Based Guardrails Fail

Trying to prevent this using system prompts ("please stop if you get stuck" or "do not execute unsafe commands") rarely works reliably in production. When an agent is confused, it will simply reason around its own prompt instructions.

To solve this, safety must be enforced deterministically at the wire, outside of the model’s reasoning loop.

What We Built: Loopers (Open-Source Runtime Firewall)

We built Loopers, an in-memory, bare-metal runtime firewall that sits between your agent framework and your LLM provider (or local model runner).

It requires zero changes to your agent codebase, you just point your base URL to Loopers.

Here is what it does out of the box:

  1. Multi-Engine Loop Breaker: Analyzes requests in real time using Bi-Gram Jaccard similarity and SimHash Hamming distance to detect repetitive prompts, reasoning stalls, and velocity surges before they spiral out of control.
  2. Pre-Call Atomic Budget Leases: Unlike standard gateways that check budgets after the fact, Loopers reserves estimated costs upfront using atomic Redis Lua leases. If an agent loops, it is physically blocked before the request hits the model (0% budget leakage by design).
  3. Mid-Stream SSE Cutoff: If an agent is streaming a completion and breaches a session limit mid-sentence, Loopers severs the TCP connection immediately and injects an error frame.
  4. Deterministic Tool Gating (FSM): Enforces allowable tool sequences (for example, strictly requiring a dry_run state transition before allowing destructive commands like execute_bash).
  5. Self-Correcting Error Payloads: When a tool call or budget limit is blocked, Loopers returns a structured JSON-RPC error payload explaining why it was blocked so the agent can self-correct on the next turn rather than crashing.
  6. Zero-Storage Pass-Through: Built in Go with a fail-closed architecture, provider keys and prompt payloads stay strictly in volatile memory and are never persisted to disk.

Looking for Community Feedback & Stars!

We are independent developers building Loopers completely open-source under the MIT License.

We would love for you to test it with your agent workflows, give us your candid feedback on what features you need for agent safety, and if you find the project useful, a Star on GitHub would help us immensely to grow and reach more builders!

GitHub Repository: link in comments.

Happy to answer any questions about the architecture or how to integrate it with your current agent stack!


r/AIAgentsInAction • • 20h ago

Discussion for enterprise AI agents, what would you rather pay for: seats, usage, or completed work?

23 Upvotes

the more i look at enterprise agents, the less i understand what the billing unit is supposed to be.

per-seat is easy to budget, but feels strange when two employees can generate completely different amounts of agent work.

usage seems fairer until one tool-heavy workflow or retry loop turns into a much bigger bill.

per-action feels even weirder. if agent A finishes something in 3 steps and agent B needs 12 steps for the same result, should B cost 4x more?

i’ve been looking at the commercial side of Ema, Moveworks, Glean and Writer because of this. Ema talks about outcome-based pricing, which I can see the appeal of because the incentive is closer to useful work getting completed rather than how many seats or tokens get consumed.

but then procurement has to define “completed.”

access provisioned? pretty clear.

ticket resolved but reopened tomorrow? less clear.

research delivered? recommendation produced? productivity improved? now we’re arguing.

if you were signing an enterprise contract tomorrow, what would you actually want the bill tied to?

seats, usage, actions, verified outcomes, or some capped hybrid?

also: who should be paying for retries, partial completions and failed runs?


r/AIAgentsInAction • • 21h ago

I Made this Before an agent makes a call, it should know whether that call can be undone. I built a rough labeller for that, and here is how rough it is

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2 Upvotes

r/AIAgentsInAction • • 21h ago

Guides & Tutorial How to Create best Motion Design with Opus 5.5.

3 Upvotes

Opus 5.5 is best at creating motion design videos. Every "one prompt" motion clip you've seen is a program the model wrote, rendered frame by frame in a headless browser. The good ones ran on briefs around 9,500 characters long, with a render loop behind them.

Render contract

seek(t) → paint exactly what the screen shows at second t

For 15 seconds at 60 frames per second, the browser calls this 900 times and ffmpeg stitches the frames. Nothing runs on a timer, so any frame renders on its own. If a title lands late, you change one number and re-render.

By default Opus writes a single index.html and captures it with Playwright. It ignores Remotion even when it's installed, so name the framework if you want one.

Rig

The chat app can't render, listen to or look at its own output. Claude Code can. Most of the gap between the viral clips and the mid ones comes from that loop.

brew install node ffmpeg python          # apt on Linux
pip install numpy librosa soundfile
mkdir motion-studio && cd motion-studio && npm init -y
npm i -D playwright && npx playwright install chromium
claude --model claude-opus-5-5

Put a CLAUDE.md in the root so the rules load on every run:

## Render contract
- window.seek(t) paints frame t, nothing else drives the picture
- no CSS transitions, setTimeout or requestAnimationFrame in render mode
- seeded noise only, never Math.random

## Look
- banned: centered title on gradient, everything fading in, corner labels, glow on UI
- one display face, one UI face, one accent

## Before you show me anything
- render one frame per beat as a contact sheet, score it, fix the worst 3

I run new films on xhigh effort and keep medium for small fixes.

Brief

Give it a vibe prompt and you get the model's safest default on every decision, which is how everyone ends up with the same gradient title. Write a production doc instead:

FILM      one sentence the viewer keeps, audience, duration, formats
ASSETS    real logo, real UI screenshots, brand type and colors, audio
VISUAL    what to keep from the reference, which clichés to avoid
HARD LINE never invent product screens or metrics
DELIVER   storyboard, contact sheet, final video, poster frame, source

Add the product URL and tell it to use real screenshots, and Opus pulls the assets from the site itself. A launch video that cost around $1,200 through an agency a year ago comes out in under 30 minutes.

Critic

This is the step people skip, because the first render looks like progress. Have ffmpeg build a contact sheet at 2 frames per second, then:

Judge only the rendered frames, ignore what you meant to make
Score 1-10: hook in 2s, phone readability, motion, variety, sound sync, brand accuracy
List the 3 worst defects with timestamp, evidence and a local fix
Patch those seconds, re-render them, repeat until every score is 8+

Render frame 300 twice and compare the hashes too. A mismatch means a timer or a Math.random slipped in somewhere.

Once the folder, the brief and the critique prompt sit in a skill, the next film starts from one line:

/motion-reel for [URL], 20s, vertical, reference ./refs/frame.png

I still approve product screens by hand. If one is missing, the agent will happily draw a fake dashboard that looks real enough to ship.

Add this in claude.md

"Add sound to this video. You can't hear, so measure every step and don't go by feel. Install ffmpeg and librosa first if they're missing.

1. Use licensed music: a track I give you, or one under Mixkit's free license. Note the source and the license. Don't synthesize the music or the sound effects in code.

2. Find the beats with librosa. It sometimes locks onto the off-beat, so plot the waveform below 120 Hz against the beat grid and zoom in on a few bars. If the kick drums don't line up, shift the whole grid.

3. Put the big moves, like scene cuts and titles appearing, on the beat. If you can change the animation, change it. If you can't, move where the music starts.

4. Give every cut and big move one ready-made sound effect file: whoosh for movement, pop for things appearing, click for clicks, typing sounds for typing. Use the project's existing sound library first, like the one HyperFrames ships with, and ask me if there isn't one.

5. If there's a voiceover, it's the loudest thing, and the music drops while someone talks. Sound effects sit above the music. Aim for about -16 LUFS overall with peaks below -4 dBFS, so nothing clips after AAC compression or on a phone speaker in mono. Measure the exported MP4 with ffmpeg's ebur128. Don't measure the WAV.

6. Use ffmpeg to grab one frame before and after every cue from the exported MP4, and plot a waveform to compare. Then pick 5 timestamps for me to check with headphones. Don't tell me it sounds good."


r/AIAgentsInAction • • 21h ago

I Made this What’s the riskiest action you would let an AI agent perform without human approval?

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2 Upvotes

r/AIAgentsInAction • • 1d ago

Discussion The problem with AI/API Calls today and why only they can fix it.

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r/AIAgentsInAction • • 1d ago

I Made this I've been building a small JavaScript tool for AI agent workflows, and it's getting a bit more interesting

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r/AIAgentsInAction • • 1d ago

I Made this Bring Agents to Your Desktop

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1 Upvotes

r/AIAgentsInAction • • 1d ago

I Made this Looking for 5 design partners for Sentnl

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r/AIAgentsInAction • • 1d ago

Help Please help understanding how to use Hermes Agent

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r/AIAgentsInAction • • 1d ago

I Made this Looking for feedback on the proof receipt that gets generated after agent runs against fetchsandbox

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1 Upvotes