Custom AI Agent Development
Soft Pyramid builds custom AI agents for US businesses that want routine work handled inside the tools they already use. Our agents answer support questions, move data between systems, prepare sales follow-ups and process documents, with a person approving the steps that matter. We plan, build and support each agent from Frisco, Texas.
Talk to a delivery leadWhat is a custom AI agent?
A custom AI agent is software that uses a large language model to decide what to do next, then takes that action through tools you have approved. It reads a request, looks up the context it needs in your systems, chooses a step such as updating a record or drafting a reply, and checks the result before moving on.
The word custom matters. A general chat assistant knows nothing about your customers, your pricing rules or the way your team hands work from one person to the next. A custom agent is built around one of your workflows. It connects to your CRM, help desk, ERP, inbox or database with its own credentials and only the permissions that workflow needs.
An agent is also different from a fixed automation. A rule-based workflow follows the same path every time and stops when the input does not match. An agent can read messy input, such as a free-text email or a scanned form, and pick the right path. Many useful agents combine both: fixed automation for the predictable steps and a model for the steps that need judgment.
- Works inside your existing tools instead of asking your team to adopt a new one
- Acts only through actions you approve, with permissions scoped to the workflow
- Sends uncertain or high-impact steps to a person for review
- Logs what it read, what it decided and what it changed, so your team can audit it
What custom AI agents do for your team
The best first agent takes on a task your team repeats every day, where the rules are mostly clear but the input is messy. These are four common places to start.
Customer support agents
A support agent reads incoming tickets, chats or emails, looks up the customer and their order or account, and drafts an answer from your own help content. It can tag and route the ticket, fill in missing fields and hand the conversation to a person when the question is outside what it is allowed to answer. Your team reviews drafts until you are comfortable letting simple replies go out on their own.
Operations agents
Operations agents handle the work that sits between systems. They watch for new orders, status changes or exceptions, check the data against your rules, update the next system and tell the right person what changed. Typical jobs include reconciling records between a CRM and an ERP, chasing missing information, preparing daily summaries and flagging anything that does not match.
Sales agents
A sales agent helps your team respond faster without losing the personal touch. It can research a new lead from the details they submitted, enrich the CRM record, score the lead against criteria you define and draft a first follow-up for a salesperson to send. After a call, it can turn notes into CRM updates and next steps so the pipeline stays current.
Document workflow agents
Document agents read invoices, forms, contracts, applications and other files, pull out the fields your team needs and check them against the rules for that document type. Clean documents flow straight into the next system. Documents with missing or conflicting information go to a review queue with the problem already highlighted, so a person only spends time where judgment is needed.
How we build a custom AI agent
Every agent goes through the same four stages. Each one ends with something your team can see and judge before we move on.
Discovery
We start with one workflow and the people who run it today. Together we map the steps, the systems involved, the decisions that need judgment and the ones that must stay with a person. We look at the data the agent will rely on and the access it will need. You get a written plan that covers scope, approach, risks and how we will judge whether the agent is working.
Prototype
Next we build a working prototype against real examples from your workflow, not a slide deck. Your team tries it, tells us where it gets things wrong and where it saves effort. This is where we settle which model to use, which steps stay as fixed automation and where human approval belongs. If the prototype shows the idea will not pay off, we tell you before you spend more.
Production hardening
A prototype that works on good days is not ready for production. We add error handling, retries, rate limits and timeouts for every system the agent touches. We scope credentials to the minimum the agent needs, add approval steps for actions with real consequences and build test sets from your examples so changes can be checked before release. Then we deploy to your infrastructure or ours, as agreed.
Monitoring and improvement
After launch we watch what the agent does. Logs and traces show each decision, so when something goes wrong we can see why. We track the cases that went to a person, the answers your team corrected and the cost of model calls. That feedback shapes the next round of changes, whether that is better instructions, a new tool, a different model or a step that should go back to a person.
If you would rather not run the agent yourself after launch, our Agent as a Service option has us host, monitor and improve it for you.
The stack we build agents on
We pick tools for the workflow, not the other way around. These are the ones we use most, and they work well together.
n8n
n8n is a workflow automation platform with built-in AI agent nodes. It is a strong fit when the agent mostly connects business tools and your team wants to see and adjust the flow visually. We run it self-hosted or in the cloud, with credentials, error workflows and logging set up for production. Our CEO, Fakhar Khan, is an n8n Community Ambassador for Dallas-Fort Worth and a published n8n workflow creator.
LangChain and LangGraph
When an agent needs more control than a visual workflow gives, we write it in code with LangChain, and use LangGraph for multi-step agents that branch, loop, keep state or pause for human approval. This suits agents with complex reasoning, retrieval over large document sets or behavior that needs automated tests.
Laravel
Agents need ordinary software around them: an admin screen to review drafts, a queue for approvals, an API for other systems to call and an audit log. We build that layer in Laravel, so the agent fits into an application your team can maintain.
OpenAI and Anthropic models
We build with models from OpenAI and Anthropic and choose per task, weighing quality, speed, cost and your data policies. We keep the model behind a clear interface so it can be swapped when a better option arrives or your requirements change.
MCP (Model Context Protocol)
MCP is an open standard for connecting AI models to tools and data. We can expose your internal systems as MCP servers with defined permissions, so agents and AI assistants your team already uses can work with them in a controlled way.
Products we have built and support
An agent is only as dependable as the software around it. These are products Soft Pyramid has built, from an AI guide that talks to prospects to the Laravel applications and integrations that agents plug into.
EQAI
EQAI is an AI-powered client acquisition and engagement platform for high-touch service businesses. Its branded multilingual AI guide, Q, answers visitor questions, helps qualify intent and hands teams prospects who are ready for the next step. Soft Pyramid built the product, including assessments, guided setup and client journey tools, and continues to support it.
ClaimTrax
ClaimTrax is a live custom software product at myclaimtrax.com. Soft Pyramid built it and provides ongoing care.
RaiseBerry
RaiseBerry is a SaaS fundraising platform built on Laravel and Livewire. Fundraisers create campaigns and sell products, and the platform tracks each sale with a shareable link per supporter so donors can see how much of each purchase goes to the cause.
Custom AI agent development in Dallas-Fort Worth
Soft Pyramid has been building software since 2013 and is led from Frisco, Texas. For companies in Frisco, Plano, Dallas, Fort Worth and the rest of Dallas-Fort Worth, that means a delivery lead in your time zone who can sit in the room for discovery workshops and walk your team through the agent before it goes live.
Through Fakhar Khan’s role as an n8n Community Ambassador for Dallas-Fort Worth, we stay close to the local automation and AI community. We also work with companies across the United States, with the same written plans, regular demos and support after launch.
Questions about custom AI agents
What is a custom AI agent?
A custom AI agent is software that uses a language model to work through one of your business workflows. It reads requests, looks up context in your systems, takes approved actions such as updating a record or drafting a reply, and passes uncertain or high-impact steps to a person.
How long does it take to build a custom AI agent?
It depends on the workflow. The main factors are how many systems the agent has to connect to, whether those systems have usable APIs, how clean and accessible your data is, how many decisions need human approval, what security review your company requires and how quickly your team can test and give feedback. We give you a written plan after discovery rather than a number up front.
Should we build a custom AI agent or buy an off-the-shelf tool?
Buy when an existing product already handles your workflow and connects to your systems; it is usually faster and cheaper. Build when the work depends on your own rules, data or tools, when several systems must be connected, or when you need control over permissions, data handling and audit logs. In discovery we will tell you if an existing tool is the better choice.
Should our agent be built in n8n or LangChain?
n8n suits agents that mainly connect business tools and that your team wants to see and adjust visually. LangChain and LangGraph suit agents with complex reasoning, long-running state or a need for automated tests in code. Many projects use both, with n8n handling integrations and a coded agent handling the reasoning.
How do you stop an agent from taking the wrong action?
We give the agent only the permissions its workflow needs, require human approval for actions with real consequences, validate its output before anything is written to your systems and log every decision. Test sets built from your own examples are run before each change is released.
Which AI models do you use?
We build with models from OpenAI and Anthropic and pick per task based on quality, speed, cost and your data policies. The model sits behind a clear interface, so it can be changed later without rebuilding the agent.
Do you only work with companies in Dallas-Fort Worth?
No. Soft Pyramid is led from Frisco, Texas, and we work with companies across the United States. Teams in Dallas-Fort Worth can meet us in person for discovery and reviews; everyone else works with us through video calls, shared documents and regular demos.
Weighing the options in more depth? Read build an AI agent in-house or hire a company.
Have a workflow in mind for an AI agent?
Tell us what your team does by hand today and which systems it touches. A delivery lead will talk it through with you, say plainly whether an agent is a good fit, and outline what discovery would cover.
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