Marketing Agents for Marketing Teams
An agent is an AI system with tools and permission to use them. For marketing teams that means research, reporting, QA and data hygiene work that runs without someone driving it.
Book a ConsultationWho this is for
- Teams losing hours to recurring research and reporting tasks
- Organisations with CRM data quality problems
- Agencies running the same QA checklist across many accounts
Typical stack
Agents we commonly build
We scope agents narrowly. A narrow agent with three tools is reliable; a general assistant with thirty is not.
- Competitor and market research agents with cited sources
- Weekly performance reporting agents that write to Slack or email
- Campaign QA agents checking links, tracking and consistency
- CRM hygiene agents that deduplicate and enrich records
- Content repurposing agents working from an approved source
Permissions and auditability
Agents receive scoped credentials, write actions require explicit approval where the blast radius is large, and every tool call is recorded for audit.
Where a marketing agent makes sense
Use an agent where a human repeats a defined checklist or judgment across many similar items. We start with a pilot that produces a measurable time saving and a clear approval path, then scale. Areas that pair well with agents include:
- Campaign QA prior to launch across many assets and channels
- Weekly or monthly performance summaries that cite the underlying data
- CRM cleanup tasks like deduplication and enrichment with audit trails
- Competitive research from a curated set of sources with links
- Brief preparation from approved inputs (never from a blank page)
How this connects to your stack
Agents are only useful when they read and write to the systems you already use. We wire tools into your marketing automation, analytics and CRM layers so outputs land where work happens.
- Marketing automation and lifecycle workflows (see Services: Marketing Automation)
- Analytics and reporting surfaces for cited, shareable summaries (see Services: Marketing Analytics)
- Internal tools and data stores via APIs and webhooks (see Services: AI App Development)
Success criteria and guardrails
We define success up front in minutes saved, error rates reduced or coverage increased. Every agent ships with an evaluation set, confidence thresholds, review queues for risky actions, and cost ceilings so spend never surprises you.
Frequently asked questions
How are agents kept from taking damaging actions?
Scoped credentials, an allow-list of tools, approval gates on destructive or outbound actions, and full logging of every call.
What is MCP?
The Model Context Protocol is an open standard for exposing tools and data to AI clients. We use it so the same tools work across ChatGPT, Claude and other clients.
Delivered through these services
AI Marketing
AI applied to segmentation, content operations and decisioning
AI App Development
Production AI applications, internal tools and agents