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How do you deploy AI agents for financial institutions without compromising on security, isolation, or scale? Rogo powers complex research, analysis, and deal workflows for leading financial firms, supporting complex tasks across tens of thousands of concurrent users. To meet the diverse needs of financial teams, Rogo relies on... both specialist and general-purpose agents that can adapt to a wide range of workflows. With Claude Managed Agents and self-hosted sandboxes, Rogo gets the orchestration and post-training capabilities needed to design these agent systems, while E2B provides the execution layer underneath. Every piece of code generated by an agent runs inside its own E2B sandbox with full auditability and traceability. Each sandbox is powered by an isolated microVM, providing the kernel-level isolation critical for Rogo's customers. The result is a seamless agent experience backed by the security, reliability, and compliance requirements that financial institutions demand.show more

Vasek Mlejnsky

9,001 subscribers

16,858 views • 5 months ago •via X (Twitter)

Science & Technology Finance

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9 Comments

Vasek Mlejnsky5 months ago

Check out the full case study with our partners at @AnthropicAI and @RogoAI here:

Matt Brockman5 months ago

Building agent systems for finance is a different game. New ways of interacting with sandboxes emerging depending on workload, still early!

Tomas‎ Valenta‎5 months ago

Big thanks to the Rogo team for sharing this! Helping customers get the infra layer right and seeing what they build on top of it is one of the most rewarding parts of this job.

Mikayel5 months ago

Best in class infra + best in class harness, used by an elite team as they build a generational product. What more can someone wish for in a case study!

Jason Haugh5 months ago

One small thing that's helped us: routing the low-confidence work to a person before it reaches the client. The traceability covers the after, and a live human check on the shaky calls keeps it safe in the moment.

Niel Pal5 months ago

Rogo is killing it.. Awesome job all around.

hodisr5 months ago

Sandbox is the easy half. Research agents read. Action agents source, diligence, monitor, work out. Their failure mode isn't a security boundary. It's that the data they act on doesn't agree with itself. The hard part of agent deployment is the dataset, not the runtime.

チョウサイ5 months ago

Sounds nice, but is it actually battle-tested? Or just theory? 🤔

Baba.Festi4 months ago

Isolation answers "can the agent run safely." The half teams scope too late: "is it allowed to do this exact thing, right now." Sandbox plus a policy check on each action is what clears a bank's risk team. How does Rogo handle that second part?

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Vasek Mlejnsky

23,085 views • 3 months ago

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Vasek Mlejnsky

23,210 views • 2 months ago

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72,189 views • 2 months ago

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18,949 views • 4 months ago

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Lit Protocol 🔑

42,110 views • 1 year ago

A snapshot of the Rogo Spreadsheets Agent. We’re proud to have built an agent that produces deliverables you’ll actually use. With it, you can: • Build complex, multi-tab DCFs / LBOs • Generate cohort, retention, P&L & KPI analyses • Convert filings / uploaded docs into structured data Every output is grounded in high-fidelity financial data — with every number attributed back to its source. Available in Rogo today.

A snapshot of the Rogo Spreadsheets Agent. We’re proud to have built an agent that produces deliverables you’ll actually use. With it, you can: • Build complex, multi-tab DCFs / LBOs • Generate cohort, retention, P&L & KPI analyses • Convert filings / uploaded docs into structured data Every output is grounded in high-fidelity financial data — with every number attributed back to its source. Available in Rogo today.

Rogo

12,259 views • 9 months ago

AI agents can already generate text, analyze data and execute tasks. But what happens when they need to hire each other and get paid for the work? This is the part of TermiX I’ve been digging into lately. On TermiX, a job has two sides: a client agent that needs something done and funds the job, and a worker agent that actually executes it. Once the quote is accepted, the payment and agreed terms sit between them onchain. Neither side can simply rewrite the deal afterward. And this isn't presented as an abstract concept. The TermiX agent marketplace already shows agents with their own identities, reputation, completed jobs and settled value. Some of the agents shown have completed hundreds of jobs, with tens of thousands of dollars settled. Think about where this model can go. A research agent could outsource data collection to another agent. A developer agent could hire a security-focused agent. A marketing agent could hand translation or design work to specialized agents. Instead of one AI trying to do everything, specialized agents can discover each other, agree on a job, execute it and settle the transaction. Humans built marketplaces around people hiring people. TermiX is building the infrastructure for agents hiring agents. That’s where autonomous AI starts looking a lot more like an actual economy.

AI agents can already generate text, analyze data and execute tasks. But what happens when they need to hire each other and get paid for the work? This is the part of TermiX I’ve been digging into lately. On TermiX, a job has two sides: a client agent that needs something done and funds the job, and a worker agent that actually executes it. Once the quote is accepted, the payment and agreed terms sit between them onchain. Neither side can simply rewrite the deal afterward. And this isn't presented as an abstract concept. The TermiX agent marketplace already shows agents with their own identities, reputation, completed jobs and settled value. Some of the agents shown have completed hundreds of jobs, with tens of thousands of dollars settled. Think about where this model can go. A research agent could outsource data collection to another agent. A developer agent could hire a security-focused agent. A marketing agent could hand translation or design work to specialized agents. Instead of one AI trying to do everything, specialized agents can discover each other, agree on a job, execute it and settle the transaction. Humans built marketplaces around people hiring people. TermiX is building the infrastructure for agents hiring agents. That’s where autonomous AI starts looking a lot more like an actual economy.

holly

13,718 views • 2 months ago

Introducing Claude agents in Notion. Your task board is Claude’s to-do list. Anthropic runs the model and the agent harness. Notion is the orchestration layer: context, UI, and a shared place for teams to review and ship, and kick off Claude workflows. Join the waitlist:

Introducing Claude agents in Notion. Your task board is Claude’s to-do list. Anthropic runs the model and the agent harness. Notion is the orchestration layer: context, UI, and a shared place for teams to review and ship, and kick off Claude workflows. Join the waitlist:

Notion

496,001 views • 7 months ago

Communities can now build with a coordinated team of AI agents. Agent execution is live on connecting community decisions to the agents that carry them out. $ZZZ brings the full workflow into one workspace: Discussion → decisions → tasks → execution. 1. Members chat and vote. An agent creates tasks from those discussions and records the decisions. 2. The lead agent prioritises tasks, clarifies the details with members and presents interactive mockups for approval. 3. The lead agent coordinates a swarm of agents to complete the tasks. Watch a community discussion become a task, then see the agents get to work.

Communities can now build with a coordinated team of AI agents. Agent execution is live on connecting community decisions to the agents that carry them out. $ZZZ brings the full workflow into one workspace: Discussion → decisions → tasks → execution. 1. Members chat and vote. An agent creates tasks from those discussions and records the decisions. 2. The lead agent prioritises tasks, clarifies the details with members and presents interactive mockups for approval. 3. The lead agent coordinates a swarm of agents to complete the tasks. Watch a community discussion become a task, then see the agents get to work.

zzz

15,894 views • 1 month ago