AI agent
AI agent engineering
Agents that own a task end to end: they read, decide, call your tools and report back.
What you get
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Tool use
Agents call your APIs and get things done.
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Retrieval
Grounded in your documents, with citations.
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Structured output
Results your systems can parse and trust.
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Human handoff
Escalates to your team when unsure.
Where it fits
Every ticket triaged and drafted first
- 1 New ticket
- 2 Classify intent
- 3 Research account & docs
- 4 Draft reply
- 5 Human review if unsure
Tools it would use:
helpdesk
crm.lookup
search_docs
Inbound documents into clean records
- 1 Document arrives
- 2 Extract fields
- 3 Validate & dedupe
- 4 Write to ERP
- 5 Flag exceptions
Tools it would use:
ocr
erp.create
rules.check
Research accounts and prepare every call
- 1 Meeting booked
- 2 Gather account history
- 3 Summarise news
- 4 Write brief
- 5 Log follow-ups
Tools it would use:
calendar
crm.search
web.fetch
Agents that know your codebase
- 1 Alert or PR opened
- 2 Read code & logs
- 3 Propose a fix
- 4 Run the tests
- 5 Open a pull request
Tools it would use:
git
ci.run
logs.query
Good questions
What is the difference between an agent and a chatbot?
A chatbot answers. An agent acts: it plans steps, calls your tools and systems, checks the result and reports back, or hands over to a person when it should.
What makes a process a good first candidate?
Repetitive work with clear inputs and a way to check the result: triage, extraction, research, reporting. We help you pick the first one.
Which models do you work with?
We choose the model per task: hosted models from providers such as Anthropic and OpenAI, or open-weight models on your own infrastructure. Your data, latency and budget decide.