AI Agent Development
Custom AI agent development for real business workflows
We build AI agents that do more than chat. They read context, call approved tools, take defined actions in your systems and escalate to a person when the rules say so. Every agent we ship has a narrow job, measurable outcomes and an audit trail.
- Least-privilege tool access
- Evaluated on your real examples
- Full logs and handoff
Jobs an agent can own end to end
Lead intake and follow-up
Respond to new inquiries in seconds, ask qualification questions, create or update the CRM record and book a call.
Inbox and ticket triage
Classify incoming messages, extract order numbers or addresses, draft replies and route work to the right queue.
Scheduling
Check availability, propose times, create calendar events and send confirmations and reminders.
Back-office tasks
Reconcile form submissions with spreadsheets, prepare weekly summaries and flag records that need attention.
Research and preparation
Gather account context before a sales call and summarize it in the CRM.
When an agent is right, and when it isn't
If a task follows exactly the same steps every time, a conventional automation or script is usually cheaper and more predictable, and we'll tell you so.
- High volume: a task your team already understands well.
- Unstructured inputs: emails, chat messages or call transcripts that need judgment.
- A safe fallback: handing off to a person is always possible.
- Measurable: time to first response, hours saved or qualified leads routed.
Engineering discipline, not prompt guesswork
- 01
Workflow map
Inputs, systems, decisions, exceptions and the person accountable for each step.
- 02
Tool design
A small set of well-described actions, such as
find_contactorcheck_availability, each with least-privilege credentials. - 03
Evaluation set
Real, anonymized examples with expected outcomes, to measure accuracy before and after every change.
- 04
Guardrails
Input screening, action limits, human approval for high-impact steps, rate limits and spending caps.
- 05
Observability & handoff
Logs of every conversation and tool call, alerts, documentation and full ownership transfer.
Model-agnostic, security-first
Agents that can act need stronger controls than chatbots. We design against the risks in the OWASP Top 10 for LLM Applications, especially prompt injection and excessive agency.
Models
- Anthropic Claude
- OpenAI
- Google Gemini
- Open-weight models
Orchestration
- Python
- Node.js
- n8n
- Webhooks
Hosting
- Your cloud or ours
- Secrets manager
- Monitoring
AI agent questions
Related: AI chatbot development · dashboards and portals · hosting and monitoring
Can an AI agent work inside our existing CRM and tools?
Yes, when those tools offer an API, webhooks or a supported automation connector. We confirm access, rate limits and plan requirements during scoping.
How do you stop an agent from making costly mistakes?
Narrow permissions, explicit escalation rules, confirmation for irreversible actions, evaluation against real examples before launch and monitoring after launch.
Do we need our own data or model?
Usually no custom training is needed. Agents use your existing records and documents through retrieval and tool calls, with access limited to what the workflow requires.
What does it cost to run an agent?
Mainly model usage, hosting and third-party API fees. We estimate them from your expected volume and add usage monitoring so costs stay visible.
Scope your first AI agent
Tell us about the workflow or product you have in mind. In a free 30-minute call we confirm fit, risks and a sensible first milestone, with a written scope before any work begins.
Free call · Scope agreed before work starts · US, UK, CA, AU