AI Agent Development for Work That Needs Judgement.
We build custom AI agents that read the situation, call the right tools in your CRM, helpdesk, and back-office systems, and finish the task. Or hand it to your team when they should. Every agent ships with evals, guardrails, and full tracing.
Not sure you need an agent? Read AI agent vs chatbot or see our workflow automation service.
What an AI agent is
A chatbot talks. A workflow follows a map. An agent decides.
An AI agent is a language model with a goal, a set of tools, and a loop. It reads the situation, picks a tool, looks at what came back, and decides what to do next. It keeps going until the job's done or it hits a rule that says a person should take over.
That's different from a chatbot, which answers questions but rarely does anything in your systems. It's also different from workflow automation, where you draw every step in advance and the software follows that path exactly. Workflows are faster and cheaper when the steps never change. Agents are worth it when the right next step depends on what the last one found.
Here's a concrete example. A customer emails: "I was charged twice and my order still hasn't shipped." A workflow can tag it "billing" and route it. An agent can check Stripe, confirm the duplicate charge, refund it, look up the shipment in your warehouse system, see it's stuck at a carrier hub, reply with the tracking link and an apology, and log all of it on the ticket. If the refund is above your limit, it drafts everything and waits for a person to approve.
Most real systems mix all three. We'll often put a small agent inside a larger workflow automation so the predictable steps stay cheap and only the judgement calls go to the model.
The agent loop
- 01
Goal
A trigger arrives: a new ticket, a form fill, an invoice in the inbox, a request in Slack.
- 02
Plan
The model reads the input and your instructions, then decides what it needs to find out or do first.
- 03
Act
It calls a tool: look up the customer, search the docs, check stock, draft a reply.
- 04
Observe
It reads what the tool returned and decides whether it's done, needs another step, or should stop.
- 05
Finish or hand off
It completes the task and logs what it did, or passes the case to a person with full context attached.
| Chatbot | Workflow automation | AI agent | |
|---|---|---|---|
| What it does | Answers questions in text | Runs a fixed sequence of steps | Works toward a goal, choosing its own steps |
| Who decides the next step | A script or retrieval match | The path you drew in advance | The model, within the limits you set |
| Takes actions in your systems | Rarely | Yes, the same ones every time | Yes, picked from a scoped tool list |
| Handles messy or unexpected input | Poorly | Breaks or routes to a person | Adapts, or escalates when unsure |
| Cost per run | Lowest | Low and predictable | Higher and more variable |
| Best for | FAQs and simple lookups | Stable, high-volume processes | Judgement calls across several systems |
AI agents for business
Six agents we build most often.
Each one does a specific job with a specific set of tools, and each one knows when to stop and ask a person. We don't build general-purpose "do anything" agents. They're hard to test and harder to trust.
Support triage agent
Reads every inbound ticket, pulls the customer's plan and order history, tags intent and urgency, and resolves the routine ones: order status, password resets, refund requests under your limit. Everything else lands with the right person, already summarised.
Tools: Zendesk, Freshdesk, Intercom, Shopify, Stripe
Hands off on: Refunds over a set amount, angry customers, legal or safety issues
SDR and lead qualification agent
Researches each new lead, enriches the record, scores it against your ideal customer profile, and writes a first-touch email that references something real about the company. Hot leads get routed to an AE with a brief. Cold ones go into nurture.
Tools: HubSpot, Salesforce, Apollo, Clearbit, LinkedIn data, Gmail
Hands off on: Sending to named enterprise accounts, pricing questions
Back-office operations agent
Handles the requests that bounce between ops, finance, and HR: vendor onboarding, access requests, purchase approvals, month-end reconciliation checks. It gathers what's missing, fills the forms, and chases people in Slack until the task is closed.
Tools: NetSuite, Xero, QuickBooks, Jira, Slack, Google Workspace
Hands off on: Payments, contract changes, anything that moves money
Document processing agent
Reads invoices, contracts, claims, and application forms, extracts the fields you care about, checks them against your records, and flags mismatches. Unlike template-based OCR, it copes with layouts it hasn't seen before and explains why it flagged something.
Tools: Email inboxes, SharePoint, Google Drive, your ERP or CRM
Hands off on: Low-confidence extractions, policy exceptions
Research and analysis agent
Answers questions that need several sources: competitor pricing changes, regulatory updates, account research before a renewal call. It searches, reads, cross-checks, and writes a cited summary so your team can verify every claim in seconds.
Tools: Web search, internal docs, data warehouse, RAG index
Hands off on: Final review before anything is published or sent externally
Voice agent
The same agent design, over the phone. It answers calls, books appointments, qualifies callers, and updates your CRM in real time, with sub-second responses and a live transfer to your team when a caller needs a person.
Tools: Vapi, LiveKit, Retell, Twilio, your calendar and CRM
Hands off on: Live transfer on request or when the agent is unsure
Voice AI agents →How we build agents
The model is the easy part. The rest is engineering.
A demo agent takes an afternoon. A production agent that your ops lead trusts with real customers takes tool design, testing, limits, and monitoring. This is where most agent projects stall, and it's where we spend most of our time.
Tools and function calling
An agent is only as good as its tools. We design each one like an API for a new hire: a clear name, a tight input schema, useful error messages, and the smallest permission set that gets the job done. Ten well-designed tools beat fifty vague ones every time.
MCP for shared integrations
When several agents, or tools like Claude and Cursor, need the same systems, we expose them through a Model Context Protocol server. The integration gets built once, with OAuth, role-based access, and audit logging, and every compliant client can use it.
MCP server development →Orchestration that fits the job
LangGraph for stateful agents that need checkpoints and approval steps. OpenAI Agents SDK or Claude Agent SDK when you want handoffs and tool use with little glue code. CrewAI for role-based research crews. Plain code when a framework adds nothing.
Evals before launch and after
We turn your real historical cases into a scored test suite: task success, tool-call accuracy, escalation behaviour, cost per task. It runs on every prompt, tool, or model change, so an upgrade that quietly breaks refunds gets caught before your customers see it.
Guardrails in code, not just in prompts
Scoped permissions, schema validation on every input and output, caps on steps and spend per task, PII redaction before data reaches the model, and prompt-injection checks on anything the agent reads from outside. The prompt asks nicely. The code enforces it.
Human-in-the-loop by design
You decide which actions need approval. The agent pauses, sends the proposed action to Slack, Teams, or a review queue with its reasoning attached, and resumes when someone clicks approve. As trust builds, you widen what it can do on its own.
Observability from day one
Every run is traced end to end: the input, each model call, each tool call with arguments and results, latency, tokens, and cost. When something goes wrong you can replay exactly what the agent saw and why it chose what it chose.
Single agent or multi-agent?
Start with one. A single agent with good tools handles most business tasks, and it's far easier to test. We split into multiple agents only when there's a real reason: different permission levels, different models for different steps, or a task too long to fit in one context. When we do, a supervisor agent routes work to specialists and every handoff is traced.
When you shouldn't use an agent
If every case follows the same steps, a plain workflow is cheaper, faster, and easier to audit. If the task is just answering questions from your documents, a RAG system or chatbot will do. We'll tell you on the discovery call if an agent is the wrong tool, and we'll scope the simpler thing instead.
Tech stack
Model-agnostic, framework-agnostic.
We pick tools after we understand the task, and we design so you can swap a model or vendor later without a rebuild. The eval suite tells you whether the swap helped.
Models
Agent frameworks
Tool layer
Memory and retrieval
Evals and observability
Deployment
Process
A working agent on your own data in the first month.
Discovery and the PoC take 2–4 weeks. A typical production agent goes live 2–4 months after the first call, and you only commit to the full build once the PoC numbers justify it.
Discovery call and scoping
A 30-minute call to pick one job for the agent, map the systems it needs, and agree on what success looks like in numbers. You leave with a fixed PoC scope and price.
Proof of Concept on real data
We build the agent with the minimum tools it needs and run it against 50–200 of your real past cases. You get a scored report: what it got right, where it failed, and what it costs per task.
Production build
Hardened integrations, scoped permissions, approval steps, the automated eval suite, tracing, and dashboards. Deployed into your cloud or ours.
Shadow mode, then go live
The agent proposes actions while your team approves them. Once accuracy holds above the agreed threshold, it starts acting on its own for low-risk cases.
Operate and improve
Weekly failure reviews, new edge cases added to the eval set, and retesting before any model or prompt change ships. Optional retainer, never required.
Pricing
Fixed scope. Fixed price. No hourly billing.
These ranges match the rest of our pricing. You get a firm quote after the discovery call, before any work starts.
Proof of Concept
$5K–$15K
2–4 weeks
One agent, one job, your real data. Ends with a scored eval report and a clear build or stop recommendation.
Production agent
$15K–$60K
4–10 weeks
A single agent in production with real integrations, guardrails, approval steps, evals, and monitoring. Price depends mostly on the number of systems and how much autonomy it has.
Multi-agent programme
$100K+
4–12 months
Several coordinated agents across departments, shared MCP tool layer, governance and audit logging, and an engineering team on call.
Running costs are model API fees plus hosting, billed to your own accounts with no markup. They scale with volume and with how many steps each task takes, so we measure cost per task during the PoC and give you a monthly estimate before you commit. Voice agents add per-minute telephony and speech costs; see voice AI pricing. For agents embedded in automation workflows, see automation pricing.
Related services
Agents rarely work alone.
FAQ
AI agent development questions.
Have a job you'd hand to an agent?
Bring one task and a rough sense of volume. In 30 minutes we'll tell you whether an agent fits, which systems it needs, and what the PoC would cost.