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Agentic AI & Autonomous Workflows

Turn repeatable, multi-step processes into managed AI workflows that retrieve information, take approved actions and involve your team when judgement matters.

Overview

From answering questions to moving work forward.

A chatbot answers a question. An agentic system works towards a goal. It plans the next steps, retrieves the information it needs, coordinates specialist agents when useful and calls only the business tools it has permission to use. The result may be a reconciled invoice, a qualified lead or a service request moved to resolution.

Autonomy is never all-or-nothing. We define what each agent may access, which actions it can take on its own, the limits that trigger approval and the named person who owns exceptions. Every run records its inputs, sources, decisions, actions, confidence and cost.

We start with one high-volume workflow and an agreed baseline. The system launches with tight boundaries and human checkpoints; autonomy expands only where evaluation and live evidence show that it is safe and useful.

When it fits

Where agentic AI makes business sense.

  • Skilled people doing repetitive work

    Experienced staff spend hours copying data between systems, chasing approvals and answering the same questions.

  • Rule-based automation keeps breaking

    Scripts and RPA bots fail whenever an email, form or document doesn't match the template.

  • Work waits outside office hours

    Leads, requests and tickets sit overnight because nobody is there to triage them.

  • Know-how walks out the door

    When a key person leaves, the way a task really gets done leaves with them.

What ships

What ships with every agentic workflow.

  • Workflow and agent design

    Your experts' way of working captured as reusable, testable skills, roles and hand-offs.

  • Scoped tool access

    Typed, permissioned connections to your CRM, ERP, email, documents and APIs. Every call is logged and revocable.

  • Human approval gates

    Approval thresholds and escalation paths agreed before launch, so people sign off what matters.

  • Run observability

    A record of every run: inputs, retrieved sources, steps taken, confidence and cost.

  • Evaluation suite

    Test sets and regression checks, so changes to prompts, models or tools ship like code.

  • Production operations

    Monitoring, cost budgets, alerting and drift checks from day one.

Use cases

Agentic workflows we build.

Sales and lead qualification

The agent engages new enquiries from your website, ads and WhatsApp, asks the qualifying questions your best salesperson would ask, enriches the record, scores the lead and books a meeting or routes it to the right person in your CRM.

  • Instant first response, day and night
  • Consistent qualification criteria
  • CRM records created and updated automatically

Process

How the engagement runs.

  1. 01

    Select

    One high-volume workflow with a measurable outcome and a clear owner.

  2. 02

    Ground

    Connect the data and documents the work depends on, permissions intact.

  3. 03

    Design

    Capture the decision rules; define tools, limits and checkpoints.

  4. 04

    Evaluate

    Test against real historical cases before touching live work.

  5. 05

    Run

    Launch inside the live workflow with people approving key actions.

  6. 06

    Extend

    Measure against the baseline, widen autonomy where earned, add workflows.

Interactive

Try it on your own workflow.

Two quick tools to size up AI for your business before we talk.

Deliverables

What you keep.

  • Agent code, prompts and skill definitions
  • Tool integrations and access policies
  • Evaluation sets and test results
  • Runbooks, dashboards and alerts
  • Training for the people who supervise the agent

Stack

Technology we work with.

Models

  • OpenAI GPT
  • Anthropic Claude
  • Google Gemini
  • Meta Llama
  • Mistral

Orchestration

  • LangGraph
  • Model Context Protocol
  • Python and Node services

Integrations

  • CRMs and ERPs
  • Email and calendars
  • WhatsApp Business Platform
  • REST and GraphQL APIs

Operations

  • Tracing and evaluations
  • Cost and latency budgets
  • Role-based access control

FAQ

Frequently asked questions

What makes an AI system agentic?

An agentic system works towards a goal across several steps: it can reason about what to do next, use approved tools, maintain task context and involve other agents or people. Its permissions, limits and escalation rules are defined before it runs.

How is an AI agent different from RPA?

RPA follows fixed rules and breaks when the input changes. An agent understands unstructured inputs like emails and documents and can decide between steps. We often combine them: agents handle interpretation, while deterministic automation handles steps that must never vary.

How do you stop an agent doing something it shouldn't?

Agents get only the tools and data they need, every action is permission-checked, high-impact actions need human approval and every run is logged. Limits such as refund amounts or record types are enforced in code, not left to the model.

Which model do the agents run on?

We are model-agnostic. We choose a model per task on quality, speed, cost and data-handling requirements, and design the system so models can be swapped without a rewrite.

What happens when the agent isn't sure?

It stops and asks. Confidence thresholds and escalation rules route uncertain cases to a named person with the context needed to decide quickly.

More capabilities

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