AI Agents & Automation

Automation Agents

Business workflow automation across your CRM, documents and operational systems, with defined triggers, permissions, approval points and recovery paths. Use AI where the task needs it.

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Agents that do real work, under your control

In many operations, finance and support teams, much of the day goes to moving information between systems: reading invoices, onboarding customers, preparing reports, keeping records in sync. We build AI agents that combine language-model reasoning with your APIs, so they can handle inputs that rigid rule-based automation can't. Each agent gets a defined job, only the permissions that job needs, approval points for consequential actions, an audit trail, and an interface where your team can supervise, correct or take over.

AI-assisted, expert-led agent engineering

How AI assists

  • Coding agents draft integration code, tool definitions and workflow logic within the scope engineers set.
  • AI drafts evaluation cases from permitted examples, including edge cases and attempts to misuse the agent.
  • AI groups failed and corrected runs, so engineers can trace recurring problems to their cause.
  • AI helps write runbooks and documentation from the agreed workflow and permission design.

What our experts own

  • Which actions an agent takes on its own, and which wait for a person's approval
  • Permission scopes, credentials and the data each agent can read or change
  • Failure handling: retries, fallbacks, escalation and the point where a person takes over
  • Evaluation criteria, and the go-live decision for each workflow

One supervised agent run

Illustrative run; which actions need approval, and who approves them, is agreed when we map your workflow.

  1. Trigger

    A new invoice email, form submission or scheduled check starts a run and is logged as received.

  2. Agent prepares action

    The agent reads the input, gathers context through its tools and proposes an action with its evidence.

  3. Permission check

    Policy rules confirm the action fits the agent's scope, spending limits and allowed systems.

    Checkpoint: Out-of-scope actions are blocked here

  4. Human approval

    Consequential actions, such as posting a payment or emailing a customer, wait for a named reviewer.

    Checkpoint: Reviewer approves, edits or rejects

  5. Execute

    Approved actions run through the agent's own scoped credentials, and each system's response is checked.

  6. Audit log

    Input, proposal, approver, outcome and time are recorded, so your team can trace what happened and why.

When something fails: If a step fails or the agent is unsure, it stops and hands the task to a person with full context.

Who hands work to an agent

Your staff copy details from emails and PDFs into several systems and chase the same follow-ups each week, and fixed-rule automations break when an input looks different. You want that work off their desks, without software moving money, changing records or contacting customers unchecked.

  • Finance teams keying supplier invoices into an accounting system by hand
  • Operations leads whose onboarding steps span a CRM, email and spreadsheets
  • Teams moving an agent pilot from demo to everyday use

What you receive

From mapped workflow to monitored agent

  • Agents for defined workflows

    Each agent handles a specific job, such as invoice processing, customer onboarding, ticket triage or report preparation, with clear inputs, outputs and stopping points.

  • Orchestration with approvals

    Multi-step workflows across CRM, ERP and custom systems, with conditional logic, approval steps before consequential actions, and error handling at each step.

  • Supervision and takeover console

    A review screen where your team approves, corrects or takes over tasks, with the agent's inputs, proposed actions and sources in view.

  • Evaluation suite

    Test cases from permitted real examples and edge cases, scored before every release, so prompt, model or integration changes don't quietly break a workflow.

  • Scoped system integrations

    Connections to Salesforce, HubSpot, QuickBooks or your own APIs through webhooks and scoped credentials, so each agent reaches only what its job requires.

  • Audit log, monitoring and alerts

    A record of every action, alerts on failures or unusual volume, and escalation to a named person when a step can't complete.

What agent builds don't include

  • A customer-facing assistant on your website or WhatsApp is AI Chatbots; agents here run back-office workflows, though they can draft customer messages for approval.
  • Systems without a usable API, or records too inconsistent to act on, need fixing first; we scope that as API Development or Application Modernization & Stabilization.
  • Hosting open-weight models on infrastructure you control is a separate service, Private AI Infrastructure; agents here can use it or an approved hosted model.
  • If you only want an existing agent tested for permissions and prompt injection, with no rebuild, AI Evaluation & Testing covers it and can be booked on its own.

Typical automation requests

Typical scenarios we scope, not client case studies.

  • Supplier invoices arriving by email

    Invoices arrive as PDFs in different layouts. We would build an agent that reads each one, matches it to a purchase order and prepares the entry, while mismatches and first-time suppliers wait for a finance approver.

  • Onboarding spread across several tools

    After a contract is signed, staff retype the same details into billing, the CRM and a welcome checklist. We would have an agent prepare those records and draft the welcome email, for a person to approve before anything is saved or sent.

  • A prototype agent nobody can supervise

    An internal agent works in demos but uses an admin key and logs nothing. We would split its job into scoped tools, add approval points and a run log, and test it on real cases before wider use.

How we build an automation agent

  1. 01

    Map the workflow

    We map the current process with your team, choose the steps worth automating, and agree what the agent may do alone and what needs a person's approval.

  2. 02

    Design controls and tests

    Integrations, permissions, approval steps, fallbacks and the supervision interface are designed together, along with the test set that defines a good result.

  3. 03

    Build and run in parallel

    Engineers build the agent and integrations, QA scores it against the test set, and a parallel run beside the manual process shows where it holds up.

  4. 04

    Launch in stages, then improve

    Go live on a limited scope with monitoring and team training, then widen coverage and permissions as results allow, under an agreed support plan.

Two ways to work with AI tools

Choose where AI coding agents may process your code while we build. The engineering standard is the same either way.

Not sure? We'll recommend one during scoping. Compare AI delivery options

How design, QA and operations support your agents

  • Design for review and takeover

    Designers shape the approval queue, correction screens and takeover flow, so your team can see what an agent did and why, and step in without hunting for context.

  • QA with evaluation sets

    QA tests output quality, tool permissions and failure handling against the evaluation set, and reruns regression checks whenever prompts, models or integrations change.

  • Operations with clear visibility

    DevOps deploys agents with scoped credentials, logging, alerting and cost tracking, on hosted models or private infrastructure where your data rules require it.

  • Improvement after launch

    We review failures and corrections with you, update prompts, tools and tests, and widen an agent's responsibilities only when its results support it.

FAQ

Frequently asked questions

What kinds of work can automation agents handle?

Repetitive digital work with clear inputs and a result someone can check: invoice and document processing, onboarding steps, ticket triage, report preparation and syncing records between systems. Agents can also prepare work that needs judgment, such as a draft reply or a proposed refund, for a person to approve. If a rule-based automation such as a Zapier workflow would do the job better, we'll tell you.

Will an agent act without anyone checking?

Only within the permissions you approve. Together we decide which steps run on their own and which wait for a person, such as payments, record deletions or messages to customers. Every action is logged, alerts flag failures, and your team can pause an agent or take over a task at any point. Autonomy is widened step by step, based on results, never assumed from the start.

How do we know an agent is worth it?

We agree measures before building, such as time per case, errors found in review and how often staff correct the agent, and compare them with the manual process during the parallel run. Running costs, including model usage, are tracked from the first release, so you judge the agent on evidence rather than estimates.

Where do the agent and our data run?

That's agreed during scoping. An agent can use a hosted model, such as OpenAI's GPT models or Claude, under agreed account and data-retention settings, or an open-weight model such as Llama or Mistral on infrastructure you control. Our development work follows the package you choose: Private / Local AI Engineering or Claude Code / OpenAI Codex Engineering.

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Start with one workflow

Tell us which process slows your team down, or which existing agent needs a review. We'll map what an agent can safely take on and where people stay in control.

Discuss your projectMap your first workflow