I Compared 6 AI Agent Platforms. I'm Not a Developer. Here's What Actually Works.

Every week there's a new AI agent platform. Every week someone on LinkedIn tells you it's going to change everything. Every week you feel more behind.

I know the feeling. I spent the last month testing six different platforms to figure out which ones actually work for someone like me — a business person who builds AI systems for clients, not a software engineer who writes code for a living.

Here's what I found. No hype. No affiliate links. Just honest observations from someone who has to ship working systems to real businesses.

The six platforms I tested

• OpenClaw — the open-source agent that runs on your own hardware
• Google ADK — Google's Agent Development Kit
• Agno — Python framework with built-in production runtime
• Cloudflare Agents — edge-native agent framework
• AWS Bedrock Agents — Amazon's managed agent service
• n8n — visual workflow automation (the wildcard)

Let me be upfront about two things. First, I am not a developer. I can't write Python from scratch. What I can do is describe what I want to an AI coding assistant (Claude Code, Codex) and review what it produces. Second, I have a production agent running on OpenClaw that has been working daily for months. So I'm not starting from zero.

What I learned: the landscape in plain English

The first thing I realised is that "AI agent platform" means completely different things depending on who's selling it. The confusion isn't your fault. The industry hasn't standardised its language yet.

Here's the simplest way to think about it. There are four layers, and every product sits in one or more of them:

Layer 1: The brain. Which AI model does the thinking? Claude, Gemini, GPT, Llama. This is the part most people already understand.

Layer 2: The framework. How do you build the agent's logic? Its instructions, its tools, its workflows. This is like the architect's blueprint.

Layer 3: The runtime. Where does the agent actually live and run? Your server, a cloud service, the edge. This is like the building the agent works in.

Layer 4: The security. Who makes sure the agent only accesses what it should? This is the lock on the door.

Most platforms cover one or two layers. Very few cover all four. That matters when you're building something for a client who asks "but is my data safe?" and you need a real answer, not a hand wave.

Platform by platform: the honest version

OpenClaw: the one I actually use

OpenClaw is what powers my daily AI assistant. You install it on a machine, give it a personality file, connect it to Telegram, and it works. No coding required — it's all configuration files.

What's good: Fast to set up. Telegram integration is native. The personality system (SOUL.md) means the agent actually feels like it has character. Cron jobs and scheduled tasks work reliably. Zero recurring cost — it runs on hardware I already own.

What's limited: It's fundamentally a single-agent system. If you want multiple agents collaborating on complex tasks, you're working against the grain. There's no built-in monitoring dashboard, no evaluation tools, no visual way to see what the agent is doing. And it's JavaScript only, which limits the AI coding assistant's ability to help in some scenarios.

Bottom line: Excellent for a personal AI assistant or simple automation. Not the right tool for multi-agent systems you'd deploy for a client.

Google ADK: the most capable, with a learning curve

Google's Agent Development Kit is the most technically impressive platform I tested. It supports four programming languages (Python, TypeScript, Go, Java), has native multi-agent orchestration, built-in evaluation tools, and a developer UI for debugging.

The killer feature: it's the only framework with native A2A (Agent-to-Agent) protocol support. Google co-created A2A, so their implementation is the reference standard.

What's good: Multi-agent is a first-class concept — SequentialAgent, ParallelAgent, LoopAgent let you compose complex workflows. The dev UI shows you exactly what each agent is doing. Evaluation tools let you measure quality systematically. And it runs on Gemini, which I'm already using — so zero additional model cost.

What's limited: You need to write code. Python, TypeScript, or another language — but code nonetheless. With Claude Code writing it for me, this was manageable but not trivial. There's no production UI you can give to a client — the dev UI is for developers, not end users. And deploying means setting up your own infrastructure or using Google Cloud.

Bottom line: The best technical foundation for serious multi-agent systems. If you're building AI solutions for clients and need agents that collaborate, this is the strongest option. The learning curve is real but manageable with AI coding assistants.

Agno: the one that includes everything

Agno takes a different approach. Instead of "here's a framework, figure out the rest," they give you the framework AND a production runtime (AgentOS) AND a control plane UI for monitoring.

What's good: The control plane is genuinely impressive — your client can log in and see what agents are doing, review sessions, manage knowledge bases, check metrics. This is the only platform where the client-facing monitoring is built in, not something you have to build yourself. It claims to be 529x faster than LangGraph at agent instantiation (their own benchmark — I haven't verified this independently). Privacy-first design — everything runs in your cloud.

What's limited: Python only. $150/month for a live connection to their control plane, plus $95/month for each additional connection and $30/seat. No native A2A support (they use MCP instead, which is different). And you're dependent on their control plane UI — if they change direction or shut down, your monitoring disappears.

Bottom line: The most complete "all-in-one" package. If you want to deploy agents for a client and give them a dashboard to see what's happening, Agno is currently the fastest path. The recurring cost is modest relative to what you'd charge a client.

Cloudflare Agents: surprisingly interesting

Cloudflare's entry was the surprise of my testing. Their Agents SDK runs on their edge network — meaning your agent executes in the data centre closest to your user. In the UK, that means London.

What's good: JavaScript/TypeScript native (matches my existing knowledge), aggressive pricing on AI inference (Llama models at a fraction of the cost of API providers), and Durable Objects give agents persistent state that survives restarts. The edge execution means sub-50ms latency for UK users.

What's limited: Young ecosystem. Far fewer examples and community resources than Google ADK or LangGraph. No multi-agent orchestration built in — you'd have to build that yourself. And the model selection is limited to open-source models (Llama, Mistral) unless you bring your own API key.

Bottom line: Worth watching. If the ecosystem matures, the combination of edge speed and low cost could be compelling for latency-sensitive applications.

AWS Bedrock Agents: the enterprise option

I need to be honest: I gave up on Bedrock fastest. It's designed for teams with dedicated AWS infrastructure engineers.

What's good: If you're already all-in on AWS, it integrates with everything — S3, DynamoDB, IAM, CloudWatch. The guardrails system is sophisticated. Knowledge bases are managed for you.

What's limited: The configuration complexity is extraordinary. CloudFormation templates, IAM policies, VPC configurations — each step assumes deep AWS expertise. The pricing is per-invocation and adds up quickly. And the vendor lock-in is near-total: your agents are deeply embedded in AWS services that have no equivalent elsewhere.

Bottom line: If your client is a large enterprise already on AWS, this makes sense. For a 20-person accountancy firm in Hampshire, it's like hiring a commercial airplane to go to the corner shop.

n8n: the one that actually ships today

Here's the thing nobody talks about in the agent platform discourse: most real-world AI automation doesn't need a multi-agent framework. It needs workflows that connect tools and run reliably.

n8n does this better than anything else I've tested. It's visual. It's self-hostable. It has native integrations with hundreds of services. And when you need AI, you add a node that calls Gemini or Claude, and it just works.

What's good: I can build a working automation for a client in hours, not days. The visual interface means clients can see what happens at each step. Self-hosted means their data stays on their infrastructure. The community is massive and helpful.

What's limited: It's not an agent framework. There's no memory, no reasoning, no autonomous decision-making. If your use case requires an AI that adapts and learns, n8n alone won't cut it. It's automation, not agency.

Bottom line: For most small business use cases today — data processing, email automation, report generation, CRM workflows — n8n is what actually gets deployed. The agent frameworks are where the industry is heading. n8n is where most businesses should start.

So which one should you care about?

If you're a business owner reading this, here's my honest recommendation:

Start with n8n or similar workflow tools. Get one process automated. See the ROI. Build confidence. This works today, it's proven, and it delivers measurable time savings within weeks.

Watch Google ADK and Agno. These are where business AI is heading — multi-agent systems that handle complex, multi-step workflows autonomously. ADK for technical power and A2A readiness. Agno for a complete package with client-facing monitoring. Both are maturing rapidly.

Ignore the noise. You don't need to evaluate every new framework that launches. Most won't exist in 18 months. The platforms backed by Google (ADK), strong open-source communities (OpenClaw, n8n), or well-funded startups (Agno) are the ones worth tracking.

The AI agent landscape is consolidating. The companies winning are not the ones with the most GitHub stars. They're the ones building things that work — for real businesses, with real data, solving real problems.

That's what I focus on too.

Sources and methodology

• All platforms tested between 1–22 March 2026
• OpenClaw: production usage since December 2025 (daily agent on personal server)
• Google ADK: quickstart + multi-agent prototype built with Claude Code assistance
• Agno: crawled documentation and architecture review (50 pages, March 2026)
• Cloudflare Agents: documentation review and architecture assessment
• AWS Bedrock: configuration attempted, abandoned due to complexity
• n8n: production usage for client automations since January 2026
• Market data: Pawel Jozefiak, "The AI Agent Gold Rush" (February 2026, 127 data points from 65 sources)
• UK adoption data: British Chambers of Commerce, "Future of Work: AI in the Workplace Report" (March 2026, 668 businesses)
• NVIDIA NemoClaw: official press release, nvidianews.nvidia.com (16 March 2026)
• Performance claims (Agno benchmarks): self-reported by agno.com, not independently verified