How to Build AI Agents: A Guide for Business and Finance Teams
Building an AI agent no longer means writing code. Here is how to build AI agents, the two paths (frameworks vs. no-code platforms), the steps from goal to production, and what it takes to build agents you can actually trust in finance.
A few years ago, building an AI agent meant a team of engineers stitching together models, tools, and code. That is still one path, but it is no longer the only one. Today, business and finance teams build AI agents on platforms, without writing code, and deploy them on real work. This guide explains how to build AI agents: the two paths available, the steps that apply either way, and what it actually takes to build an agent you can trust in a function like finance.
What does it mean to build an AI agent?
Building an AI agent means configuring a system that can take a goal, plan the steps to reach it, use tools and data to act, and produce a result, within guardrails you define. Unlike a simple chatbot, an AI agent does work: it connects to systems, takes actions, and adapts. Building one is less about training a model from scratch and more about giving a capable model the right tools, data, instructions, and controls.
Building an agent is not building a brain. The reasoning already exists in the model. Building an agent is giving that reasoning a job, the tools to do it, the data to do it with, and the guardrails to do it safely.
Two paths: frameworks vs. no-code platforms
There are two fundamentally different ways to build AI agents, and which one is right depends on who you are:
| Code / frameworks | No-code platforms | |
|---|---|---|
| Who it is for | Software engineers | Business and finance teams |
| How you build | Write code with frameworks (LangChain, CrewAI, etc.) | Configure agents in a platform |
| Flexibility | Maximum, build anything | Scoped to the platform's domain |
| Time to value | Weeks to months | Hours to days |
| Maintenance | You own the code and upkeep | The platform handles it |
The framework path gives developers total control but requires engineering time and ongoing maintenance. The no-code path lets the people who actually understand the work, the finance team, the ops team, build agents directly on a platform. For a function like finance, the no-code path is usually the right one, because the hard part is not the code; it is the domain knowledge and the controls. See the best AI agent builders for the platform options.
How to build an AI agent, step by step
Whichever path you take, building a useful agent follows the same arc:
- 1. Define the goal and workflow. Be specific about what the agent should accomplish and the steps the work involves. "Reconcile the bank account and flag exceptions" is a buildable goal; "do finance" is not.
- 2. Connect the data and systems. The agent needs access to the systems it will read from and act on, the ERP, data warehouse, banking, or billing data.
- 3. Give it tools. Define the actions it can take: pull a report, post an entry, send a message, run a calculation.
- 4. Set guardrails and approvals. Decide what the agent can do on its own and where a human must approve. This is non-negotiable for high-stakes work.
- 5. Evaluate. Test the agent against real cases to confirm it gets the right answer before it touches production.
- 6. Deploy and monitor. Put it to work, keep a human in the loop, and watch its performance over time.
What it takes to build agents you can trust in finance
In finance, a working agent is not enough. The agent has to be defensible, which raises the bar on how you build it:
- Traceability. Every output has to trace back to source data so it can be defended to an auditor.
- Human-in-the-loop. Approval gates at the decisions that matter, not blanket autonomy.
- Evaluation. Outputs validated against tests built for your business, not taken on faith.
- Access control and audit trails. Agents operating under the same controls as a human employee.
These are the pillars of AI agent governance, and they are far easier to adopt from a platform built for them than to engineer yourself. This is what Concourse provides: an AI agent platform for corporate finance where you build and deploy agents on your existing stack, with traceability, approvals, and evals built in. Rather than coding agents from scratch, a finance team configures them and puts them to work, which is why the people who operate this layer are increasingly called finance engineers.
Frequently asked questions
How do you build an AI agent?
Define the goal and workflow, connect the data and systems the agent needs, give it tools to take actions, set guardrails and approval gates, evaluate it against real cases, then deploy and monitor it with a human in the loop. You can build with code and frameworks or, more commonly for business teams, configure agents on a no-code platform.
Do you need to code to build an AI agent?
No. While developers can build agents with frameworks like LangChain or CrewAI, business and finance teams increasingly build agents on no-code platforms by configuring goals, data connections, tools, and guardrails, no code required.
How long does it take to build an AI agent?
With a framework and an engineering team, weeks to months. On a no-code platform, hours to days, because the infrastructure, integrations, and controls already exist and you are configuring an agent rather than building one from scratch.
How do you build AI agents for finance?
Use a platform built for finance that connects to your ERP, warehouse, and banking data and provides traceability, approvals, and evaluation out of the box. Define the finance workflow (close, reconciliation, forecasting), set approval gates, validate against your cases, and deploy with a human reviewing.
The bottom line
Building an AI agent today is less about code and more about giving a capable model a clear goal, the right data and tools, and the guardrails to act safely. Developers can build with frameworks; business and finance teams build on no-code platforms. In finance, the deciding factor is not whether the agent works but whether you can trust and defend what it does, which is why traceability, approvals, and evaluation matter more than the build itself.
If you want to build AI agents on your finance function without writing code, with every output traceable and a human in control, book a demo of Concourse.
