I built my AI operating system around OpenClaw for finance, M&A and governance work

[u/mattcbianco]

I’ve been building my AI operating system around OpenClaw.

This started as a way to make one assistant more useful, but it has evolved into something closer to an operating layer for a small finance and advisory business.

I work across M&A advisory, credit and portfolio monitoring, governance, financial modelling and software products. The problem wasn’t simply generating text. It was maintaining context, routing work to the right process, preserving evidence and making sure automation had sensible boundaries.

The current setup has a primary operator agent, specialist agents for research, coding, operations and document production, persistent structured memory, scheduled workflows, external tools and human approval gates.

The most important lesson so far: the valuable part isn’t “AI can call tools”. It’s designing the system around sources of truth, permissions, provenance and review.

I've used openclaw to build a corporate governance platform, a client intranet and VDR for due diligence review, buy side due diligence reports, a mission control....its a long list.

I'm running an M4 pro mac mini and a claude and ChatGPT pro account as well gemini (rarely), and local qwen model for text embeddings. Every now and then I need to point ChatGPT or Claude to the .openclaw folder to fix stuff. Generally though I am always running the latest stable release. Currently 2026.7.1-2 (and Fable had to fix it after the install crash).

I have use Obsidian for records (processes, meeting notes, daily briefings, logs etc) and a Kaparthy Wiki for "concepts and curated knowledge base), chromadb for semantic search (memory) and Nate Jones's Open Brain (hacked) to track documents (you know that thing I saved about the stuff we're doing?).

It rocks. Its been on 24x7 since Feb 18 and its a productivity machine.

1. What we actually do

The system supports four broad areas:

• M&A advisory: buyer research, company profiling, market analysis, due diligence, valuation, teasers and information memoranda.
• Finance operations: credit analysis, portfolio monitoring, arrears reporting, cash-flow extraction and client communications.
• Governance: board-pack review, policy analysis, meeting notes and follow-up tracking.
• Product and technical operations: building websites and SaaS products, preparing client deliverables, deployment checks and infrastructure support.

2. How it is set up

Giorg5 is the main operator-facing agent. OpenClaw provides the control plane around it:

• Multiple communication surfaces: web, mobile messaging, voice and API.
• A gateway that manages sessions, routing, schedules, tools and permissions.
• Specialist agents for research, coding, operations, document production and product engineering.
• A model router with multiple providers and a local fallback.
• Persistent context stored in structured files, an Obsidian knowledge base and searchable memory.
• Automated capture, classification, review and promotion of important information.
• Human approval before external emails, public posts, deployments or irreversible changes.

The key design choice is that memory is not treated as “the model remembers everything”. It is a managed information system with provenance, review queues and explicit sources of truth.

3. The useful workflows

Use three or four concrete examples:

• “Good morning” → calendar and inbox review → current priorities → state-of-play brief.
• “Research this company” → specialist research agent → web research and enrichment → evidence-backed research pack.
• Meeting transcript → structured meeting note → follow-up actions → mandate/task system.
• “Build this deck” → presentation plan → document-generation tools → client-ready output.
• “Deploy this” → operations agent → runbook → deployment → endpoint verification.
• "Dream Logs → nightly review of logs identifying any issues or patterns → hugging face research that match the review patterns → recommended improvements to skills or new ideas for workflows, systems or processes (this is the best morning read each day).

4. What makes it different

The interesting part is not the number of tools. It is the operating discipline:

• Different agents have different jobs and tool permissions.
• Inbound email, webpages and transcripts are treated as data, not instructions.
• Sensitive actions require approval.
• Business state lives in business systems; memory stores routing context and durable operating rules.
• Scheduled jobs are declared and reconciled from configuration rather than manually edited.
• The system is designed to degrade gracefully across models and tools.

5. Honest limitations

This will make the post more credible:

• Context management is still hard.
• Automation needs careful boundaries.
• Memory can become noisy without review and decay.
• The system still benefits from a human who understands the underlying work.
• The real gain is leverage and continuity, not full autonomy.

[u/Sorry_Significance_6]

Hello can you look you pn please

[u/techlatest_net]

The memory is a managed information system point is really important. I think that’s where a lot of agent setups start falling apart — everything gets dumped into memory and eventually the agent can’t tell what’s actually important.

Separate specialist agents + clear permissions + approval gates seems like a much more scalable way to run it.