:: AI services / AI agent development
AI agent development services for work that has to get done
An AI agent reads a request, looks up what it needs, decides what to do and then does it: updates a record, drafts a reply, files a ticket, books a meeting. We build custom AI agents that work inside your existing tools and ask a person before anything risky.
Works inside Salesforce, ERP and email
Human approval built in
Tested before every release
:: What we do
Custom AI agents we build
Good first agents take over a task your team does many times a day and already follows rules for.
01
Lead triage agents
Research every inbound lead, score it against your ideal customer, update Salesforce and draft the first reply for a rep to approve.
02
Service and case agents
Answer routine tickets from your help articles and order data, and pass anything unusual to a person with a written summary.
03
Document intake agents
Read invoices, purchase orders and contracts, check them against your rules and create clean records in your CRM or ERP.
04
Back-office agents
Reconcile data between systems, chase missing information by email and keep records up to date.
05
Research and reporting agents
Pull figures from several systems, answer questions in plain English and send a weekly summary with links to the source records.
06
Multi-agent workflows
Several narrow agents that hand work to each other, each with its own tools, limits and audit trail.
:: Use cases
AI agents for business: where they pay off first
The best first agents sit inside a process that already runs on rules and handles the same kind of request many times a day. Each of these links to a blueprint showing how we would plan, build and staff it.
Insurance
Claims intake agent
Reads first notice of loss emails and attachments, checks the policy, opens the claim and routes it to the right adjuster.
Read the blueprint
Finance
Accounts payable agent
Reads invoices, matches them to purchase orders and receipts, and queues only the exceptions for a person.
Read the blueprint
Recruiting
Resume screening agent
Checks every application against the role’s real requirements and ranks candidates with the evidence behind each score.
Read the blueprint
Ecommerce
Customer support agent
Answers order, delivery and returns questions from live order data and hands anything unusual to a person.
Read the blueprint
Salesforce
Case triage with Agentforce
Classifies and routes service cases, drafts replies and summarises history for whoever picks the case up.
Read the blueprint
Lending
Document processing agent
Reads bank statements, pay slips and IDs, pulls out the figures and flags anything that doesn’t add up.
Read the blueprint
:: Side by side
AI agent vs chatbot: which one do you need?
Both use large language models. The difference is what they are allowed to do.
AI chatbot
AI agent
What it does
Answers questions in a conversation
Completes tasks across your systems
Typical trigger
A customer types a message
An email, form, record change or schedule
Access to tools
Usually read-only: FAQs and documents
Reads and writes: CRM, ERP, email, calendar
Where a person steps in
Hand-off when the bot gets stuck
Approval on risky actions, set by you
Best first project
Website or support chat
One repetitive back-office task
If you need a conversation on your website or in an app, see our AI chatbot development service below.
:: Step by step
How to build an AI agent
Whether you build it yourself or with a partner, a reliable agent goes through the same eight steps. Skipping one of them is the usual reason a promising demo never makes it into daily use.
01
Choose one task with clear rules
Pick something your team does many times a day, where a good result is easy to recognise: triaging leads, reading invoices, answering order-status emails. Write down the inputs, the steps a person follows today and what done looks like.
02
Decide what it may do on its own
List every action the agent could take and sort them into three groups: allowed, allowed with approval, and never allowed. Add limits such as a maximum refund or the systems it may write to. This list becomes the agent’s guardrails.
03
Collect real examples
Gather 100 to 300 past cases with the correct outcome: the email and the reply that was sent, the invoice and the record that was created. They become the test set that tells you whether the agent is good enough.
04
Pick the model and framework
Most business agents run on a large language model from OpenAI, Anthropic or Google, coordinated with a framework such as LangGraph, or with Agentforce if the work lives in Salesforce. Choose on accuracy against your test set, cost per task and where your data may go.
05
Connect the tools
Give the agent access to the systems it needs through their APIs: CRM, ERP, email, databases. Use a service account with the narrowest permissions that work and log every call. Model Context Protocol is becoming the standard way to expose tools to agents.
06
Give it the right knowledge
Connect the policies, product data and documents it needs, usually through retrieval (RAG), so answers come from your own sources and every one can be traced back to where it came from.
07
Test before anyone relies on it
Run the whole test set, score accuracy and safety, adjust the instructions, tools or rules, and run it again. Add every case it gets wrong to the set. Don’t launch until it meets the bar you agreed in step one.
08
Launch with a person in the loop, then monitor
Start in draft mode: the agent prepares, a person approves. Track accuracy, cost and time saved every week, remove approval steps one at a time as the numbers hold, and keep monitoring because your data and policies will change.
:: How it works
How we build AI agents
Agents are only useful if you can trust them. Most of our work goes into the parts a demo skips.
01
Pick the task
One task, one team, clear rules. We write down what the agent may do alone and what needs approval.
02
Connect the tools
Secure access to Salesforce, email, ERP or databases, with the narrowest permissions that work.
03
Test like a new hire
Hundreds of real past cases run through the agent before launch, scored for accuracy and safety.
04
Launch with a person in the loop
The agent drafts, a person approves. As accuracy holds, approval steps are removed one by one.
:: Cost and timeline
How much does AI agent development cost?
Typical prices for our projects, based on a senior team at $25 to $35 an hour. We quote a fixed price for each phase after a short call, and most clients start with a pilot.
Pilot
$5k – $15k
4 to 6 weeks
One task for one team, connected to one or two systems, tested on your past cases and running in draft mode with a person approving.
Production
$15k – $40k
4 to 8 more weeks
Approval steps reduced where accuracy allows, more actions and systems, monitoring and alerts, and a handover to your team.
Multi-agent workflow
$40k – $100k+
3 to 6 months
Several agents across one process, shared guardrails, a full audit trail and an operations dashboard for the people who supervise them.
What it costs to run each month
Once live, an agent costs money every time it works: model usage, hosting and monitoring. For most single-task agents that is a few hundred to a couple of thousand dollars a month, depending on volume and the model. We measure the cost per task during the pilot, so you know it before you scale.
What moves the price
How many systems the agent reads from and writes to
How many actions it can take, and how risky they are
How much testing the risk level calls for
How clean and complete your data is
Channels and languages it has to handle
:: Before you start
Is your task ready for an AI agent?
A quick check we run on every first call. If most of the left column is true, the task is a strong first candidate.
Good first candidates
It happens dozens or hundreds of times a week
A person follows written or well-known rules
Inputs arrive digitally: email, forms, documents, records
You can tell a good result from a bad one
The systems involved have APIs
Not ready yet
Every case needs deep judgment or negotiation
The rules change weekly or live in one person’s head
A mistake can’t be caught or undone
The data it needs is scattered or mostly on paper
:: Why Sterling
What every agent we ship includes
An evaluation set built from your real cases
Guardrails: allowed actions, spending limits and blocked topics
Human approval for risky or irreversible steps
A full log of every decision and action
Monitoring for accuracy, cost and response time
:: Tools we use
OpenAI
Anthropic Claude
Gemini
LangGraph
LlamaIndex
Model Context Protocol
Salesforce APIs
Agentforce
Python
AWS
Azure
4.9
Average rating on Clutch · 350+ projects since 2019
:: Questions
Questions about AI agent development
How much does AI agent development cost?
What is the difference between an AI agent and a chatbot?
Can an AI agent work inside Salesforce?
How do you stop an agent making mistakes?
How long does it take to build an AI agent?
What is an AI agent?
Which AI model do you use?
Do we need Agentforce, or can you build a custom agent?
What does an AI agent cost to run each month?
Is our data safe with an AI agent?
Will AI agents replace our team?
:: Related AI services
Other ways we can help
AI chatbot development
Chatbots that answer from your own content
AI integration services
Connect AI to Salesforce, ERP and internal systems
AI software development
Custom AI products and internal tools, built to production
AI consulting services
Plan before you build: readiness, strategy and proof of concept
Send us your brief. We’ll help turn it into reality
A senior engineer replies within one business day with questions or a first plan: approach, team, timeline and estimate.