AI Agent Development Services
LaunchPad Lab's AI agent development services put working agents into production, not a demo that stalls. We design agents that take real action across your systems: pulling data, deciding inside the rules you set, and finishing multi-step work without a person in the loop. With 14+ years building production software and 760+ projects behind us, we get agents past the proof of concept and into daily use. Tell us the workflow you want to automate, and we'll tell you if an agent is the right fit.
How our AI agent development services go beyond chatbots
AI agents built right take action, they don’t just respond. A lot of tools marketed as AI agents today are chatbots in disguise: they answer questions and summarize content, but they still wait on a person to take the next step. Real agents move the work forward on their own. They pull data from your systems, follow business logic, make decisions inside defined guardrails, and complete tasks from start to finish. When something falls outside the guardrails, the agent escalates to a person who can resolve it, so nothing gets stuck.
This is the shift from rigid, rule-based automation to agentic AI. Instead of fixed workflows that break when something changes, agents handle variation, work with unstructured data, and act across your CRM, email, and document systems. Here’s where LaunchPad Lab differs from the typical AI agent development company: we’re product engineers first, so the same in-house team that designs your agent also ships it, connects it to your stack, and keeps it running. We’ve done exactly that for real clients, from Bullhorn’s support hub to an actuarial firm’s reporting, on top of 14+ years of AI software development.
Our AI Agent Development Services
As an AI agent development agency, LaunchPad Lab builds the full range of agents, from a single custom agent to a coordinated multi-agent system. Here is what we build and where each one earns its place.
Custom AI Agent Development
Off-the-shelf agents rarely survive contact with a real business. We build custom AI agents around your actual workflows, from a single-task agent to a coordinated crew. Each one runs on the mix of large language models (LLMs), retrieval, and generative AI that fits the job, and connects to the data and tools it needs to take action. It works inside your existing systems instead of forcing a rebuild. Built for your workflow, not a template.
AI process automation agents
The repetitive work that eats your team’s day, document processing, data entry, approvals, reporting, and email triage, is exactly what an agent should own. We build agents that run these processes end to end, deciding what happens at each step and escalating only the cases that need a person. It works hand in hand with our AI automation services: the automation moves work through your systems, and the agent decides how. Manual queues, cleared.
Conversational AI & virtual assistants
An assistant that only answers questions wastes the interaction. We build conversational agents that read context, reach into your systems, and actually do the thing, pulling a record, updating a field, kicking off a workflow, from a single request. The chat is just the front door; the work happens behind it. Answers that come with actions.
Multi-agent systems
When one process spans several systems or teams, a single agent isn’t enough. We design multi-agent systems where each agent owns a role, and they coordinate through handoffs, shared context, and clear responsibilities, the way a good team does. Complex work moves without a person stitching the steps together. Specialists that hand off cleanly.
CRM & platform agent integration
An agent is only useful where your work already lives. We embed agents directly into your CRM, internal tools, and business software, with native Agentforce support for Salesforce teams and custom API integrations everywhere else. No rip-and-replace, no parallel system to babysit. Agents inside the tools you already run.
AI agent strategy & proof of concept
Not sure an agent is the right call? We run a focused workshop to map your workflows, rank where an agent pays off first, and build a proof of concept on your real data, so you can validate the approach before funding a full build. If the discovery needs to go deeper, our AI consulting team leads it. Proof before you commit.
Security and compliance built in
Security is how LaunchPad Lab has shipped software for 14+ years, including in HIPAA-bound healthcare, audit-heavy financial services, and confidential legal work. We build every agent inside guardrails you define, then enforce them: least-privilege access to your systems, human approval on the actions that need it, and audit-ready logs of everything the agent does. Your data stays yours, and we never use it to train public models. When your policy requires it, we deploy inside your own cloud or VPC. Security is part of how we build, not a step we bolt on at the end.
How We Build AI Agents
Discovery & agent strategy
We map your workflows to find where an AI agent can have the biggest impact. We define what the agent needs to do, which systems it connects to (CRM, email, databases, APIs), and where it escalates to humans instead of acting on its own. We set clear guardrails and success metrics, so you know exactly what you’re building before we start.
Build & integration
We build the agent and connect it to your systems. We train it on your real data and test it against actual workflows so it handles edge cases. Every two weeks, you see working software that can pull data, follow business logic, update records, and complete multi-step tasks. We iterate based on what you learn in testing.
Launch & continuous optimization
We deploy to production with monitoring and human checkpoints where decisions need oversight. After launch, we refine the agent’s reasoning, add new capabilities, and expand it into additional workflows as your team finds new uses. The agent gets smarter the longer it runs.
Featured AI Agent Projects
AI-Enhanced Self-Service Hub for Bullhorn
Bullhorn needed to improve efficiency and reduce support overhead across its platform. LaunchPad Lab built an AI-enhanced self-service hub that helps users find answers, navigate workflows, and complete tasks faster without relying on support teams. The result was improved efficiency and a better user experience across the platform.
AI Workflows for an Actuarial Firm
An actuarial firm was relying on spreadsheets and manual reconciliation, with reporting cycles taking up to 60 days. LaunchPad Lab built an AI-enhanced platform that automates data intake, reconciliation, and reporting. The result was a 10x faster turnaround and a projected $2M annual profit increase.
Industries We Serve with AI Agents
We design AI agents around the specific workflows, constraints, and systems of your industry—helping teams move faster, stay compliant, and reduce operational friction.
Financial Services
We build agents that support compliance checks, speed up client onboarding, assist with portfolio adjustments, and triage fraud alerts, so your team focuses on decisions instead of manual reviews. Everything is traceable and human-reviewed where accuracy and audits matter.
Healthcare
We build HIPAA-minded agents for intake, documentation, and operational workflows, with the access controls and audit-ready outputs the field requires. Clinicians stay in the loop on anything that touches patient care.
Legal
We build agents for document-heavy review, intake, and knowledge retrieval, grounded in your own content with sources you can check.
What Our Clients Say
Frequently Asked Questions
What is AI agent development?
AI agent development is the work of building software agents that complete tasks on their own and wiring them into your business. An AI agent pulls data, follows your rules, uses tools, and finishes a job without waiting on a person. The focus is execution, not just answering questions, so work moves forward end to end instead of stopping at a suggestion. At LaunchPad Lab, we build these agents as production software, wired into your systems and taken past the proof of concept.
How does an AI agent work?
An AI agent works in a loop. It takes a goal, pulls the context it needs from your data and systems, plans the steps, then calls the right tools or APIs to act and checks the result before moving on. Memory carries context across steps, retrieval keeps answers grounded in your content, and guardrails decide when the agent acts on its own and when it escalates to a person.
How long does it take to build an AI agent?
With us, a focused proof of concept usually takes two to four weeks, and a production-ready agent a few months, depending on the integrations and complexity. We work in two-week cycles, so you see working software early and adjust as we go, rather than waiting months for a big reveal. Discovery comes first, so you have a scoped timeline before the build starts.
How much does AI agent development cost?
It depends on what you’re trying to automate. A smaller proof of concept can be a good way to test value without a large investment. A full production system costs more because it includes integrations, testing, and ongoing support. We usually define the use case first, then size the work from there.
Can AI agents handle customer support?
Yes, support is one of the fastest places agents pay off. An AI agent resolves repetitive tickets, pulls answers from your knowledge base, updates records, and escalates the rest to a person with full context. The self-service hub we built for Bullhorn cut support case volume across its platform. Your team steps in only where judgment is needed.
What are the types of AI agents?
The main types we build:
- Task agents handle one job end to end, like triaging tickets or reconciling data.
- Conversational agents act on your systems from behind a chat interface, not just answer.
- Process automation agents run multi-step, rules-based work like approvals and reporting.
- Multi-agent systems coordinate several specialized agents on a workflow that spans teams or systems.
Most real deployments combine a few of these, matched to the job rather than a fixed template.
What's the difference between agentic AI and generative AI?
Generative AI creates content: text, code, images, an answer to a prompt. Agentic AI acts. An AI agent uses generative models as one part of a larger system that plans, calls tools, and completes multi-step tasks on its own. Put simply: generative AI writes the email; an agentic system reads the request, drafts the email, updates the CRM, and schedules the follow-up. We build both, but agents are where the work gets done.
What industries benefit most from AI agents?
Any industry with repeatable workflows and manual processes can benefit. We see strong use cases in financial services, healthcare, insurance, legal, and professional services. The common thread is clear processes where automation can reduce time and improve consistency.
What is the difference between a chatbot and an AI agent?
A chatbot answers questions. An AI agent takes action. It can access systems, follow business logic, and complete tasks from start to finish. In most cases, the chatbot is just the interface, while the agent is doing the work behind the scenes.
Can AI agents work with our existing CRM and tools?
Yes. The goal is to improve the systems you already run, not replace them. If a platform has an API, we can usually connect an agent to it, along with your CRM, internal tools, databases, and third-party software. Agents read, update, and act on data inside your current workflows, so your team keeps working the way it already does.
Let’s Build Your AI Agent
Tell us about the workflow you want to automate. We’ll help you determine whether an AI agent is the right solution or if something simpler will do the job.
You don’t need a fully defined plan to get started. We can talk through what’s slowing your team down, what you’ve already tried, and where automation could make a real difference.
No hype. No pitch decks. Just a practical conversation about what’s worth building and what isn’t.