What it solves
A workflow follows fixed steps; an agent is given a goal and works out the steps — reading, deciding, calling tools, checking its own result and asking a person when the risk is real. We build agents for specific jobs with strict scopes, budgets and audit logs, so they are useful on Monday and still trusted in a year.
Typical problems
- Tasks that need judgement at every step, so no rule-based automation fits
- A chat pilot that answers questions but cannot actually do anything
- Agents that run up API bills or wander outside their brief
- No record of what the AI did, or why
- Vendors promising ‘autonomous’ when the work needs a person at two points
Example workflows
Three workflows we build regularly in this service. Names and numbers are illustrative of typical outcomes; measured results are published only in case studies.
Inbound lead research and first touch
A rep spends 20 minutes per lead reading the website, LinkedIn and CRM notes before writing a first email.
The agent researches the company, drafts a first email in your voice with sources, and queues it for one-click approval; low-fit leads are tagged and parked.
Typical result: research time removed; every lead touched the same day.
Supplier invoice reconciliation
Finance matches invoices to purchase orders by hand and emails suppliers about mismatches.
The agent matches, explains discrepancies, drafts the supplier query and books the clean ones; anything ambiguous goes to a person with the evidence attached.
Typical result: clean invoices booked without touch; exceptions handled in one queue.
Client onboarding coordinator
A project manager chases documents, sets up folders and systems, and answers ‘what happens next’ emails.
The agent collects documents, sets up the accounts, answers status questions from the plan and escalates blockers — within a fixed budget per client.
Typical result: consistent onboarding without a coordinator’s week.
Capabilities
- Task-specific agents with tool access (CRM, email, docs, calendars, APIs)
- Planning, retries and self-checks with hard limits on cost and scope
- Human checkpoints for anything irreversible or customer-facing
- Multi-agent hand-offs (researcher → writer → reviewer)
- MCP / function-calling integrations with your systems
- Evaluation sets, audit logs and cost dashboards
What every build includes
The difference between an automation that works in a demo and one that still works in a year.
- Automatic retries when a connected app is briefly unavailable, so nothing is silently lost
- A run log for every execution — what came in, what was decided, what went out
- Failure alerts to Slack or email after repeated errors, with a pause switch you control
- Idempotent steps: running twice never creates duplicates
- Written handover: a one-page map of the workflow, its owners and how to change it
- Built in your own accounts — you keep every credential, licence and asset
How the work is delivered
Map
Current process documented with the people who run it.
Scope
Written proposal, diagram, deliverables, fixed price.
Build
Short stages in your accounts, demo at the end of each.
Launch
Monitoring, alerts and documentation before go-live.
Pricing and engagement
We pick one job an agent should own end to end, define its scope, tools and the decisions that stay with a person, and show what the first week of runs would look like.
Scoped in writing after the audit. One price, one outcome, no hourly surprises. Half on start, half on launch.
Monitoring, fixes when an app changes its API, and a monthly improvement sprint. Cancel any month.
Deliverables
- Working agent in your accounts with documented scope and guardrails
- Evaluation set with pass/fail cases
- Audit log and cost dashboard
- Runbook for pausing, changing and extending the agent
Questions
What is the difference between an agent and an automation?
An automation runs the same steps every time. An agent is given a goal and chooses the steps, using tools and checking its work. We use automations wherever the steps are known and agents only where judgement is needed — and we tell you which is which.
How do you stop an agent doing something harmful or expensive?
Scope (which tools and records it may touch), budget (tokens, runs, spend per task), checkpoints (a person approves anything irreversible or customer-facing) and a full audit log. The agent can be paused from one switch.
Which models and frameworks?
Claude and OpenAI models behind an interface so they can be swapped; LangGraph, n8n or plain code depending on the job; MCP or function calling for your tools. Nothing runs on our infrastructure.
How do we know it works?
Every agent ships with an evaluation set: real examples with the expected outcome. Changes to prompts or tools are checked against it before they go live.
How does pricing work?
Every engagement starts with a free audit. You then get a fixed-scope proposal with one price and a delivery date, in USD. Ongoing monitoring and improvements are a monthly retainer you can cancel any month.
How quickly can something be live?
A first workflow is usually live within one to three weeks of sign-off. Larger builds are staged so something useful ships early and the rest follows in sprints.
What happens when a workflow fails?
Every build ships with retries, a run log and alerts. If an app changes its API, the retainer covers the fix; without a retainer we quote a small fix and turn it around quickly.
Who owns the automations afterwards?
You do. Everything is built in your accounts and documented, and your team is walked through it at handover. You can maintain it yourself or keep us on retainer.