AI Agents for Agencies and Brands

Agencies run on workflows that have not changed in a decade: briefs arrive as documents, taxonomies get built in spreadsheets, campaigns are monitored through dashboards, and reporting eats analyst hours every Monday. Each of these is structured, repetitive work with clear correctness criteria. That makes them the right first targets for AI agents, and it is exactly where we build.

Arctus: a whitelabel reporting agent for agencies

Arctus is Appliscale's productised, whitelabel agentic reporting solution. It integrates with your data warehouse and answers reporting questions directly: which line items are pacing behind, how creative variants compare across platforms, where spend shifted week over week. No dashboard hunting, no waiting for an analyst to rebuild a filter set.

Because it is whitelabel, Arctus ships under your brand. Your clients see your reporting capability; we run the agent engine underneath. It slots into your stack three ways: standalone, side by side with your existing BI tools, or as the reporting layer underneath a custom management agent we build for you.

Custom AI agents for briefs, taxonomy, media buying and reporting

Beyond reporting, we build custom agents for the workflows that consume most agency operations time.

• Brief handling. An agent parses incoming briefs, extracts objectives, budgets, flight dates and constraints, and maps them onto your campaign setup templates.
• Taxonomy building. An agent generates and validates campaign naming and taxonomy structures against your conventions, catching the inconsistencies that later break reporting joins.
• Media buying. An agent monitors pacing, budget allocation and delivery across platforms and surfaces the decisions an operator actually needs to make.
• Campaign reporting automation. An agent assembles recurring client reports from warehouse data, with every number traceable to the query that produced it.

Each agent starts with an audit of your current workflow, so we automate what your team actually does rather than a generic version of it.

Built on production agent experience

We do not prototype agents for demos. Our team ships them into production at Bedrock Platform, a next-generation DSP, where agents handle supply discovery, campaign optimization and deal troubleshooting on top of a real-time bidding core with strict latency and correctness requirements.

On the data side, our ad insights engine shows how we build the pipelines agents depend on: an AWS pipeline ingesting multi-platform campaign data through S3, Lambda and Glue with PySpark, linking performance metrics to creative attributes, delivered as a working MVP in the first iteration cycle. And our Harmonyze AI teammate, which automated legal and compliance operations at 10x ROI, proves the custom-agent model transfers across domains.

What makes an agency AI agent production grade

Agency agents touch client money and client reporting, so we hold them to production standards. Every agent ships with evals against golden datasets, so accuracy is measured before and after every change instead of assumed. Memory lets agents retain campaign context across sessions. Langfuse monitoring traces every run for reliability and token cost, and we optimise context and token usage so agents stay fast and affordable at agency volume.

Production grade also means knowing where agents do not belong. Our Head of Engineering Damian Naglak has laid out why an agent's reason-act loop cannot sit on the live bidding path: an LLM loop plans and chains steps in seconds while auctions close in about 120 milliseconds, so agents belong in planning, buying and reporting, with fast models on the hot path. And when one buyer agent can transact with fifty sellers in seconds, cross-seller frequency capping becomes your problem to engineer, not something the protocols hand you by default. We design agency agents around both constraints from day one.

The full engineering approach, from Mastra tooling to MCP servers to eval pipelines, is covered on our AI Agent Development page. The warehouse and pipeline work that feeds these agents lives under Data Engineering.

If your roadmap includes transacting through agentic protocols, we build full buyer agents, and our agencies page maps the full path from agentic strategy to a protocol-ready buyer agent.