Guide · 12 min read

Platforms for AI workflows and agentic systems: how to choose

The best platform for AI workflows or agentic systems depends on where your data lives, who will build and maintain the system and how much control you need. Automation platforms suit multi-step workflows across tools, agent frameworks suit engineering teams, CRM-native AI suits work inside one CRM, and custom builds suit needs the others cannot meet.

What kinds of platforms are there?

There are four broad categories: workflow automation platforms, agent frameworks and SDKs, AI built into CRM platforms, and custom builds using model APIs and connectors.

Vendors are named as examples of each category rather than as recommendations. Features and pricing change often, so check each vendor's current documentation before deciding.

How do the categories differ in practice?

They differ in who builds and maintains the system, how deeply they reach into your data, how much control you have and how costs behave as usage grows.

Workflow automation platforms

Visual builders that connect business tools and run multi-step workflows, with AI models available as steps. n8n's documentation covers AI agents, tools, memory and MCP servers as building blocks, and n8n can be self-hosted. Zapier offers Zapier Agents alongside its app connections, and Make is widely used for visual, multi-step scenarios. These platforms suit operations and revenue teams who want to see and adjust the workflow themselves.

Agent frameworks and SDKs

Code libraries for building agents that plan, call tools and keep state. Examples include Anthropic's Claude Agent SDK, the OpenAI Agents SDK and LangGraph. They offer the most flexibility and need engineers to build, host, test and monitor what they produce.

CRM-native AI

AI agents built into the CRM itself. Salesforce describes Agentforce as a platform for building and customising AI agents integrated with the Salesforce ecosystem, and HubSpot's Agent Hub is where teams run, build and manage AI agents inside HubSpot. Both inherit the CRM's data and permissions, which is a strength for work that lives in that CRM and a limit for work that does not.

Custom builds

Application code that calls model APIs directly and connects to your systems, often through standard connectors. The Model Context Protocol is an open standard for connecting AI applications to tools and data sources. Custom builds give you full control, and full responsibility.

Do you need an agent or a workflow?

Most B2B processes are better served by a defined workflow with AI steps. Reserve agents for work that needs flexible, model-directed decisions within guardrails.

Anthropic's engineering guidance, Building effective agents, distinguishes workflows, where models and tools follow predefined code paths, from agents, where the model directs its own process and tool use. It recommends finding the simplest solution that works and adding complexity only when it is needed.

In practice, routing, enrichment, reporting and document preparation usually run well as workflows. Open-ended research or multi-step problem solving may justify an agent, with limits on what it can access and change. The Bowtie Academy lesson on agents, chatbots and automations explains the difference in plain English.

Which criteria matter when choosing a platform?

Judge platforms on integration depth, governance and logging, hosting and data handling, the skills you have, cost behaviour, portability and monitoring.

For UK organisations handling personal data, the ICO guidance on AI and data protection should inform the hosting and data questions.

How do you govern agentic systems?

Give each agent or workflow a named owner, the least access it needs, approval steps for consequential actions, complete logs and a tested way to pause it.

• A named owner responsible for behaviour, changes and reviews.
• Least-privilege access: only the data and actions the task needs.
• Approval steps before customer-facing, financial or irreversible actions.
• Logs that record what was done, with what inputs and why.
• Limits on spend, volume and retries.
• A tested way to pause or roll back.

The National Cyber Security Centre's guidelines for secure AI system development cover design, development, deployment and operation. Our posts on what never to hand an agent and governed action and provenance go deeper on control and traceability.

When is a custom build justified?

A custom build is justified when your data, security, logic, performance or integration needs go beyond what platforms offer, and you can own its maintenance.

• The workflow spans systems that no single platform connects well.
• Security or data residency requirements rule out a shared cloud platform.
• The logic or volume exceeds what a visual builder handles reliably.
• The people using it need a purpose-built interface, such as an internal app.
• Platform usage costs at your volume would exceed the cost of owning the build.

If none of these apply, a platform is usually faster to deliver and simpler to run. When a purpose-built interface is the right answer, see our internal apps work.

How does The Sparked Group choose platforms?

We choose around your existing systems and team, often combining the CRM you use, an automation layer such as Make or n8n, and custom components where needed.

We start with the systems you already use and the people who will own the work. Our assessment checks data quality, access and integration options before we recommend what to connect or change, and ownership, hosting, third-party software and AI usage costs are agreed before work starts.

Read how we approach AI automation, see complex multi-system integrations, or find your starting point.

Frequently asked questions

What are the best platforms for building agentic systems and AI workflows?

It depends on the job. Workflow automation platforms such as Make, n8n and Zapier suit multi-step workflows across tools. Agent frameworks such as the Claude Agent SDK, OpenAI Agents SDK and LangGraph suit engineering teams. Salesforce Agentforce and HubSpot's AI agents suit work inside those CRMs. Custom builds suit needs the others cannot meet.

What is the difference between an AI workflow and an AI agent?

An AI workflow follows predefined steps, with AI used for specific tasks such as summarising or classifying. An AI agent lets the model decide its own steps and tool use within limits. Most business processes are better served by workflows.

Is CRM-native AI enough, or do we need a separate platform?

CRM-native AI works well when the process lives inside that CRM. If the workflow spans finance, communication or other systems, an automation platform or custom build usually handles the connections better, sometimes alongside the CRM's own AI.

How do we keep agentic systems safe?

Give each system a named owner, least-privilege access, approval steps for consequential actions, complete logs, spend and volume limits, and a tested way to pause it. Review its behaviour regularly against what it was designed to do.