Agents that carry the work, so your team can carry the thinking.

Agentic automation uses AI agents to carry out multi-step work with a degree of independence. Unlike traditional automation, which follows fixed rules, an agent can gather information, reason about it, decide on the next step and use tools such as your ad platforms, CRM or email system to get things done. In practice, agents take on complex but repetitive tasks such as monitoring campaigns, researching prospects or assembling reports, while people stay responsible for judgement and approval.

A chatbot answers questions in a conversation. An agent does work. It can connect to your systems, pull and analyse data, take actions within agreed limits and report back on what it has done. Many of the agents we build never talk to customers at all. They run quietly in the background, preparing analysis, drafting content or updating records, and hand their output to your team for review.

The best candidates are tasks that are repeated often, follow a recognisable pattern and draw on data that already exists in your systems. Campaign monitoring, performance reporting, competitor research, lead enrichment, content variations, enquiry triage and data hygiene are common examples. Tasks that depend on nuanced judgement, sensitive relationships or high-risk decisions are better left with people, with agents helping to prepare the groundwork.

That is not how we approach it. The aim is to remove the repetitive work that keeps skilled people from doing their best thinking. Teams that work with agents typically spend more time on strategy, creative ideas and customer relationships, and less on admin. We involve your people in designing each workflow, because they know where the friction sits and they will be the ones working alongside the agents every day.

Each agent has clearly defined permissions, so it can only read and change what it needs to. Actions that carry risk, such as spending budget, publishing content or contacting customers, require human approval. Outputs are checked against your data and brand guidelines, activity is logged so you can see what happened and why, and we monitor performance continuously. When an agent gets something wrong, we adjust the workflow rather than leaving it to repeat the mistake.

We regularly connect agents to Google Ads, Meta, TikTok, LinkedIn, Google Analytics, Shopify, Klaviyo, HubSpot, Pipedrive, Slack and common data warehouses, among others. Where a platform offers an API, we can usually integrate with it. During discovery we map the tools you use and design workflows around them, so you do not need to change your stack to benefit.

A focused agent for a single workflow can often be running within a few weeks, including testing with your team. Larger systems that span several workflows or departments take longer and are usually delivered in stages, so you see value early and we learn from real use before expanding. We start small, prove the value and scale what works.

Costs depend on the number and complexity of workflows, the systems involved and how much ongoing management you need. There is typically a build phase followed by a monthly fee covering running costs, monitoring and improvements. We set out expected costs alongside the time saved or value created, so you can judge the return clearly before committing.

Yes. AI models, platform APIs and your own business needs all change over time. We monitor agent performance, fix issues as they arise, update workflows when platforms change and suggest improvements as we learn what works. Some clients prefer to take ownership internally once a system is stable, and we can hand over documentation and train your team to do so.

Start with a conversation about where time is being lost in your team today. We will usually run a short discovery to map your workflows and tools, then recommend one or two agents that are likely to deliver clear value quickly. Once those are running and trusted, we plan what comes next together.