u/viky_shetye

I tested Hermes Agent’s new Bot Mode with 3 AI agents here’s what actually worked (and what didn’t)

Hermes Agent recently released Bot Mode, and I wanted to test whether it actually makes multi-agent collaboration easier or if it’s mostly a nicer UI for existing Hermes profiles.

So I built a small 3-agent Stocks research team:

🔎 Researcher — gathers evidence from public filings and market data
⚠️ Risk Analyst — challenges the numbers, assumptions, unsupported claims, and missing risks
📝 Thesis Editor — turns the reviewed evidence into a balanced final report

I only gave the task to the Researcher.

The Researcher gathered the evidence and handed it to the Risk Analyst. The Risk Analyst reviewed it and was supposed to hand the result to the Thesis Editor.

So instead of:

Human → Agent A → Human → Agent B → Human → Agent C

I was testing:

Human → Agent A → Agent B → Agent C → Human

And for the most part, it worked.

What I like about Bot Mode is that the agents aren’t just invisible calls in a workflow. Each bot has its own identity, role, tools, skills, memory, and persistent conversation, so you can actually inspect what each specialist is doing.

But there are some important limitations.

It’s also not a full workflow/DAG engine, and the handoffs don’t mean you’re getting guaranteed parallel execution.

My current mental model is:

🧠 Hermes Profiles = specialized isolated brains
👥 Bot Mode = gives those brains identities, persistent rooms, and communication
🗂️ Hermes Kanban = better when you need structured tasks, dependencies, and project orchestration

For complex multi-agent projects, I’d still lean toward Kanban. But for maintaining a group of persistent AI specialists that can talk and delegate to each other, Bot Mode makes the experience much easier to operate.

I recorded the entire experiment, including creating the bots, configuring them, the handoffs, the failed delegation, scheduled jobs, and the differences between Bot Mode, Profiles, and Kanban:

🎥 https://youtu.be/w3VI6zC4_0I

I’m curious how others building multi-agent systems think about this approach.

I have been experimenting with multi-agent workflows in Hermes Agent, and for this project I wanted to see if I could build something closer to a small startup intelligence/research team.

The question I gave it was essentially:

Which B2B AI SaaS markets in North America are actually worth investigating?

Instead of having one agent research everything, I created 4 separate Hermes profiles:

🧭 Startup Director — converts the question into a research brief + scoring rubric and orchestrates the workflow

🔎 Market Researcher — investigates buyer pain, demand, company formation, funding, adoption and traction

📊 Competition & Signals Analyst — researches competitors, existing alternatives and whether attention is translating into actual adoption/payment

🛡️ Skeptic Editor — audits the other agents’ claims, checks the underlying evidence, looks for counter-evidence and downgrades weak conclusions

I used Hermes Kanban to create a dependency graph where the two research agents work in parallel. The Skeptic Editor’s task has both research tasks as parents, so it cannot start until both are finished.

Since separate Hermes profiles don’t share conversation history, the agents communicate through the Kanban tasks, a shared project directory and the parent/child dependencies.

The final output isn’t just a list of “promising AI startup ideas” either.

The system generates structured CSVs for:

• ranked markets
• companies
• competitors
• evidence-backed claims
• sources/supporting passages
• validation experiments

I also made the system explicitly distinguish between what is verified, company-reported, inferred or unknown.

That turned out to be one of the most useful parts.

AI research can very easily turn:

funding → demand
customer logos → retention
market hype → willingness to pay

…and then present the conclusion with a lot more confidence than the evidence deserves.

The Skeptic Editor exists specifically to fight that tendency.

For this run, I had the agents compare 5 B2B AI SaaS markets across healthcare, software development, contact centers, finance/accounting and retail.

I recorded the entire build, from creating the Hermes profiles and skills to the Kanban orchestration and final research output:

🎥 Video: https://youtu.be/P2d-m5NcEZw

I’m curious what people working with multi-agent systems think of this architecture.