AI Agent Development Services
We build AI agents that do real work inside the systems you already run, from support triage to order workflows, with a person approving the actions that matter. Production, not demos.
AI agent
Proposes the next action
Human approval
- ERP
- CRM
- BI
- Support
EXISTING BUSINESS SYSTEMS
What Are AI Agents?
An AI agent is software that plans and carries out multi-step work toward a goal, instead of only answering questions in a chat. AI agent development services design, build, and connect that software. An AI agent pairs a large language model with a harness: the code that lets the model call APIs, query databases, and run commands, then feed the results back for the next step.
That tool-use loop is what separates enterprise AI agents from a chat window. Custom AI agents are built around one business's own systems, data, and rules instead of a generic product's. Most work best with a narrow job and a person approving actions that carry risk.
How is an AI agent different from a chatbot?
A chatbot chats; an agent does. A chatbot takes text in and gives text back, so a person still has to act on the answer. An agent can take the next step itself: look up the order, check the store hours, or file the ticket, then report what it found or propose what to do next.
USE CASES
Where AI Agents for Business Pay Off
AI agents for business pay off where people spend hours checking several systems, chasing exceptions, or re-keying information. Here's where we've put them to work.
Support triage across systems
For a national luxury florist, an agent reads each support ticket, checks the relevant systems, and finds the cause. It fixes straightforward issues on its own and escalates complex ones to a person with full context.
Frontline IT support for custom software
For a company running a large custom order, customer, and inventory app, an agent handles first-line IT requests: creating users, fixing stuck refunds, triaging tickets, and drafting replies. Fixes no longer wait weeks on an offshore team.
Order-to-install workflows
For a custom windows and home services company, an agent checks each order's sourcing, pricing, and scheduling after a sales rep enters it, cutting wrong orders and mispriced quotes. See AI & Business Process Automation.
Guided transactions on existing systems
For a large car retailer, an AI assistant drives the existing sales system instead of replacing it. Associates get a conversational guide, while the old system's rules still stop a car leaving without required documents.
Listing content from photos
For a real estate startup, an agent scans listing photos, identifies the home's features, writes the description, and fills in the MLS fields that listing agents often skip.
Incident investigation
When a client faced a bot attack, an agent reviewed the traffic and found every request used the same user agent. Blocking it stopped the attack, and a person made that call.
OUR APPROACH
From Pilot to Production
Most agent pilots stall because the demo got built and the system around it didn't. As JBS Dev puts it: "What we hand over is a working system inside the client's environment, with integrations, guardrails, observability, error handling, and human oversight built in. Demo-to-production is where most AI work dies, and it dies because the surrounding system was never built."
Demo only
- The demo got built
- The system around it didn't
Working system
Small agents, clear jobs
Our AI agent orchestration splits work across focused agents, one for orders and another for payments, instead of one super-agent. Smaller prompts perform better, cost less to run, and are easier to test and debug.
A person approves what matters
Our agentic AI development services follow a discover, propose, confirm loop. The agent does the digging and suggests an action, and a person approves or rejects it. Hours of research become seconds, without handing over control.
Hard rules stay in code
Where a mistake is unacceptable, an API enforces the rule, not the model. A car title doesn't release until every document is on file. Agents get only the permissions their job needs, and never permission to delete a database.
Built for the times it's wrong
Language models can give different answers to the same question, so we design for exceptions from day one. Anything that can't fail gets human review, and repeat mistakes are added to the agent's instructions as memory.
Provider-aware, not provider-locked.
"We pick models and platforms based on the task, accuracy needs, privacy, latency, and cost." We work with both local and frontier AI models. Leading models are close in raw ability, so the harness and integrations often matter more. Where we can, we build on your own LLM accounts and data stores, so you own the prompts, the tuning, and the data instead of renting them from each SaaS vendor.
WHAT WE BUILD
Custom AI Agents We Build
We build custom AI agents and enterprise AI agents that work inside the ERP, CRM, BI, and support systems your teams already use.
Support and service agents
Agents that read tickets, check order, logging, and back-end systems, and propose a resolution for a person to approve, so support staff stop logging into five places to answer one question.
Workflow and operations agents
Agents that check orders, quotes, and schedules against your rules as work moves through ERP and line-of-business systems, catching wrong prices and wrong orders before they cost money.
BI and data agents
Agents wired into your BI stack, such as Snowflake, that turn questions into queries, flag anomalies, and run ML models like a price sensitivity analysis, in a sandbox rather than production.
IT and infrastructure agents
Agents connected to your monitoring platforms that check health metrics, logs, and firewall rules against a reported problem, then propose the likely root cause for an engineer to confirm.
Connectors for the systems you already run
We build Model Context Protocol (MCP) connectors for internal APIs, CRMs, ERPs, and older systems, even file transfers to an AS/400, so agents can work with them. See AI Integration & MCP.
Agents where people already work
Agents delivered through Teams, Slack, email, or text, not another portal to bookmark. Adoption is how an agent gets judged, and adoption follows the tools people already open every day.
HOW IT WORKS
How an AI Agent Project Runs
STEP 1
Pick one use case
Start with one problem, often through an AI readiness assessment. We ask a simple question: if this works perfectly, does it pay for itself? If not, we find a better first use case.
STEP 2
Prove it
A proof of concept tests the agent against your real systems and data, with a feasibility analysis and projected outcomes, so you see results before committing to a full build.
STEP 3
Build and connect
We connect the agent to your systems through APIs and MCP, add the approval steps and hard rules, and keep the business logic your existing systems already enforce.
STEP 4
Launch where people work
The agent goes live in Teams, Slack, email, or the tools your team already uses, with a person approving any action that carries risk.
STEP 5
Repeat the pattern
Once one agent is calling your APIs and earning its keep, the next use cases come faster. We copy the pattern and expand from there.
What your team gets at handoff
You own everything we build: the agent, its prompts, its connectors, and the data it uses. Services we can provide at handoff include knowledge transfer sessions with your team and documentation of how the agent works, what it connects to, and where its approval rules live. Ongoing support is also available. Many agent workloads run on tools your team already maintains, such as APIs and BI jobs.
RESULTS
AI Agent Results
Published JBS Dev projects. Explore the work, approach, and results.
100s
Of employee uses each week
Industrial Products
Agentic AI Knowledge Platform for an Industrial Products Provider
Challenge: Thousands of PDF documents sat in an outdated knowledge base that was slow to search and hard to scale.
Result: A RAG and agentic AI platform on AWS replaced it. Employees use it hundreds of times a week, and customers get source-backed answers about products.
Read the industrial products case study80-90%
Complete on the first proposal draft
Safety Engineering
AI Knowledge Retrieval and Proposals for a Safety Engineering Firm
Challenge: Staff reviewed large volumes of documents by hand to answer customers and analyze risk.
Result: A GenAI system on AWS Bedrock finds answers quickly and drafts proposals that are 80 to 90% complete.
Read the safety engineering case study<10 min
Per listing, down from up to a full day
Real Estate
AI Listing Creation for a Real Estate Client
Challenge: Realtors spent hours entering MLS listings by hand on outdated platforms.
Result: An Amazon Bedrock platform and a Chrome extension cut listing creation from up to a full day to under 10 minutes.
Read the real estate case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
SECURITY
Security, Data, and Control
Your data stays under your control. Agents run in your own environment, or through enterprise AI agreements that bar the provider from training on or retaining your data. We treat AI tools like email: the same governance, under your company licenses, never personal accounts.
Data access is role-based. Each agent gets only the permissions its job needs, analysis runs in a sandbox instead of your production data warehouse, and no agent gets permission to delete a database.
Hallucinations are handled by design. Small, focused prompts keep accuracy up, hard rules live in APIs instead of prompts, and a person approves any action where a mistake would be costly. The same limits contain prompt injection: an agent can only reach what its role allows, and risky actions still need a person's approval.
When an agent gets it wrong, the mistake stops at the proposal stage, and recurring errors are added to its instructions so it doesn't repeat them. See AI Governance & Security.
YOUR ENVIRONMENT
AI agent
Role-based access
Sandbox
BEFORE ANY RISKY ACTION
Agent proposes
Mistakes stop here
A person approves
Or rejects
COST AND TIMELINE
What Drives AI Agent Cost and Timeline
Cost and timeline depend on how many systems the agent touches and how much review its work needs. A narrow agent on your existing stack is a much smaller project than a multi-system build.
- 01Systems the agent connects to
- 02Custom integration those systems need
- 03Approval steps required
- 04Human review the work needs
- 05How often the agent runs
- 06Prompt size and token usage
How the timeline is set
A first agent can move quickly when it starts narrow. Our Quick Start GenAI offering can produce a proof of concept in under 10 hours for certain scenarios. Production timing depends on how many systems the agent connects to and how much human review it needs, and the assessment or proof of concept sets it. We start with one use case and expand once it works.
How cost is set
The main cost drivers are the number of systems the agent connects to, the custom integration they need, the approval steps required, and token usage. Token costs climb when agents run constantly or carry huge prompts, so we trigger agents on events, keep prompts small, and use MCP to cut the tokens spent processing data. The proof of concept gives you real usage to estimate from.
ENGAGEMENT MODELS
Ways to Work With Us
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you know AI should help but not where. We review your workflows and systems, apply the ROI test to each candidate, and pick a first use case worth building. Our GenAI Assessment, free to start through the Quick Launch Lab, includes a proof-of-concept demo, feasibility analysis, projected outcomes, and a roadmap.
- ROI test for each candidate
- Proof-of-concept demo
- Feasibility analysis
- Projected outcomes
- Roadmap
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to build or modernize a defined system.
For a defined agent, senior engineers build it end to end: integrations, MCP connectors, approval steps, and the rules that keep it in bounds. It launches in the tools your team already uses. You own everything we build, with no license fee for an agent running on your own systems.
- Integrations
- MCP connectors
- Approval steps
- Rules that keep it in bounds
- Launch in your team's tools
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you have an existing team, services we can provide include architecture guidance, hands-on development, QA, and ongoing support as your agents expand to new use cases. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work. See ways to work with us.
- Architecture guidance
- Hands-on development
- QA
- Ongoing support
- Nearshore support option
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
WHY JBS DEV
How to Choose an AI Agent Development Company
Five questions worth asking any AI agent development company, and how we answer them.
- Have you put agents into production?
- Yes. Our agents handle frontline IT support, support triage, and order workflows for clients, not just in demos.
- Will it work with the systems we already run?
- Yes. We connect agents to your existing APIs, ERP, CRM, and BI stack, build MCP connectors where needed, and deliver agents in Teams, Slack, or email.
- Who controls our data and what gets built?
- You do. Agents run in your environment or under your enterprise AI agreements, and you own everything we build.
- What happens when the agent is wrong?
- It proposes and a person approves. Hard rules live in APIs, and repeat mistakes are added to the agent's instructions.
- Who does the work?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
AI Agent Development Services FAQs
What are AI agent development services?
AI agent development services design, build, and connect software agents that carry out multi-step work across business systems instead of only answering questions. The work covers AI agent orchestration, choosing models, connecting tools and data through APIs or MCP, setting approval rules, and handling the cases where the agent gets it wrong. The goal is an agent running inside real workflows, not a demo.
How is an AI agent different from Copilot or ChatGPT?
Copilot and ChatGPT are general assistants, while a custom agent is built around your systems, rules, and approvals. It can call your internal APIs, check your databases, and complete steps in your workflows. Because it runs on your own LLM accounts and data stores, you own the prompts, the tuning, and the data instead of paying each SaaS vendor for its own AI layer.
Does our data leave our environment?
Not outside your control. Agents can run in your own environment, and where a hosted model is used, enterprise agreements bar the provider from training on or retaining your data. We apply the same controls you use for email: company licenses only, no personal accounts, and access limited to what each agent needs.
How do you prevent hallucinations and prompt injection?
We limit what the model decides and what it can touch. Focused agents with small prompts make fewer errors, hard rules are enforced in APIs rather than prompts, and each agent gets only the permissions its role needs. A person approves actions where a mistake would be costly, so a hallucinated or manipulated instruction stops before it does damage.
What happens when the agent gets it wrong?
A person catches it before it matters. Our agents propose actions and a person approves or rejects them, so an error stops at the proposal. In some support use cases, an agent's first conclusion is wrong about three times in ten, which is exactly why we design for review. Recurring mistakes are added to the agent's instructions.
What do token costs run in production?
It depends on how often the agent runs and how much it reads each time, so the proof of concept is where we measure it. Costs climb when an agent runs constantly or carries a huge prompt. We trigger agents on events, keep prompts small, and use MCP connectors, which cut the tokens spent processing data.
Do we need to hire AI people to maintain this?
Usually not. Calling a language model is one of the simplest integrations there is: a single API request. Many agent workloads run on tools your team already maintains, such as BI jobs and APIs. Services we can provide include knowledge transfer, documentation, and ongoing support, so your team can run and extend what we build.
How long until an agent is in production?
A first agent can reach production quickly when it starts with one narrow use case. Our Quick Start GenAI offering can produce a proof of concept in under 10 hours for certain scenarios. Production timing depends on the systems involved and the review steps needed, and the assessment or proof of concept sets it.
