The Future of AI agents in finance: Beyond chatbots to virtual coders
By Deepak Sheoran, Founder and CTO
The AI industry has been obsessed with tools. MCP servers, function calling, tool libraries with hundreds of integrations. We've built elaborate systems where LLMs can call APIs, query databases, and orchestrate workflows.
But here's what we're learning: the most powerful tool — files — is already ubiquitous.
An agent with access to files, a code interpreter, and web access can accomplish virtually anything — often more flexibly than one wired to dozens of purpose-built tools. The file becomes the universal interface. The code interpreter becomes the universal tool.
This insight is reshaping how we think about AI agents at DwellFi.
Why financial services are different
Finance isn't like other domains. When a coding agent helps you refactor a codebase, the worst case is a failed build. When a financial AI makes a mistake, money moves incorrectly.
But the complexity isn't just about stakes — it's about data.
Financial data is adversarial
Every financial document is a puzzle:
- PDFs that look clean but have invisible formatting nightmares
- Excel files where the same concept appears in different columns across funds
- Scanned documents where OCR confidence directly impacts dollar accuracy
- Multi-source reconciliation where two "correct" numbers don't match
Generic AI assistants weren't built for this. They were built to be helpful, not to be correct.
The rise of the virtual coder
Here's our thesis: the future of financial AI is the virtual coder.
Not a chatbot that answers questions. Not a tool-caller that orchestrates APIs. A reasoning agent that writes and executes code to solve financial problems.
Why code? Because code is precise, auditable, and reproducible. When the agent writes Python to reconcile two position files, every step is visible. Every calculation can be verified. Every edge case can be caught and handled.
This is fundamentally different from an LLM that "just knows" the answer. In finance, showing your work isn't optional — it's the product.
The long conversation problem
There's another challenge the industry is grappling with: context windows are finite.
A user starts a conversation. They upload documents. The agent researches, plans, executes. But somewhere around message 20, the context fills up. The agent "forgets" what happened earlier. Work is lost.
The current workarounds are crude:
- Summarization loses detail
- Truncation loses history
- Starting over loses everything
We believe the answer is persistent workspace — giving agents a place to store their work that survives across sessions.
When an agent has a workspace, the conversation becomes a session within an ongoing project. The agent can save intermediate work, resume where it left off, and build on previous analysis. This is how humans work. It's time agents caught up.
Skills without code changes
The final piece of the puzzle is teachability.
Today, if you want an agent to do something new, you write code. You build integrations. You deploy updates. This is slow, expensive, and doesn't scale.
The future is configuration-as-capability.
Want the agent to behave like a "Credit Analyst"? Configure it. Want it to follow specific reconciliation procedures? Configure it. Want to restrict which data sources it can access? Configure it.
The agent remains the same. The configuration transforms its persona, capabilities, and constraints. Business users can create specialized agents without engineering involvement.
What we're building at DwellFi from day one
At DwellFi, we're building toward this vision:
1. Document intelligence as foundation
Before an agent can reason about financial data, it needs to see it. We've invested heavily in parsing complex financial documents—not just extracting text, but understanding structure, tables, and relationships.
2. Sandboxed code execution
Our agents write and execute code in secure environments. Every analysis is reproducible. Every calculation is transparent.
3. Persistent agent workspaces
Work shouldn't disappear when the conversation ends. We're building agents that maintain state across sessions, accumulating knowledge and building on prior work.
4. Configurable agent personas
The same core agent technology, infinitely adaptable. Financial analysts, compliance officers, operations teams—each gets an agent configured for their needs.
The road ahead
We're still early. The industry is still figuring out the right primitives. But a few things are becoming clear:
- Files beat tools for generality and flexibility
- Code beats prose for financial accuracy and auditability
- Persistence beats statelessness for real work
- Configuration beats code for business agility
The companies that win in financial AI won't be the ones with the most tools or the flashiest demos. They'll be the ones who build agents that work the way financial professionals work: methodically, transparently, and with an uncompromising focus on accuracy.
That's what we're building at DwellFi. Not chatbots that help with finance. Virtual coders that do finance.
DwellFi provides AI-powered automation for private equity and fund administration. Our platform helps financial professionals extract data, reconcile positions, and automate operational workflows.
The convergence
For those interested in the broader industry trends, here's where we see the major players converging:
The patterns emerging from Cursor, Claude Code, and LlamaIndex are remarkably similar. We're all arriving at the same architecture from different starting points. The question isn't what to build anymore—it's how well you can build it for your specific domain.
For financial services, that means uncompromising accuracy, complete auditability, and deep understanding of the messy reality of financial data. That's our focus. That's our edge.
Interested in how DwellFi can help automate your fund operations?