Credit: The Verge
Most businesses stop at conversational AI: Gemini, Claude, ChatGPT, Copilot etc. Handy tools, but tinkering won’t overhaul how a company actually runs. The next stage is about moving beyond chatbots and point hacks into real automation and agents: platforms that connect systems, cut drudgery, and eventually act as digital colleagues.
At Overdose, we're currently going through this process ourselves, reviewing AI Automation and Agent platforms. The exercise isn't about chasing shiny features. It's about asking: which capabilities matter most, how do these tools fit into our business, and where do they sit on the spectrum from simple workflows to true agentic systems?
This article shares the lenses we used to evaluate them, not to recommend a single “best” platform, but to help others cut through the noise and see where each category of tool fits.
Before Thinking About The Technology...
AI transformation doesn’t start with a few curious employees tinkering in the corner (it might light the spark, but not ignite the full flame). It starts with the senior leadership team. Without executive literacy, cultural alignment, and clear permission to challenge old norms, nothing sticks.
Bottom-up enthusiasm dies fast when people hit compliance, data access, legacy system roadblocks, and the team finding team to continue to innovate. Unless leaders update incentives and set the tone, innovation stalls before it even starts.
Alignment at the top matters most. Without it, marketing pulls one way, operations another, IT resists, and you end up with fragmented experiments instead of a coordinated strategy. Sometimes it takes an external perspective to make the urgency clear as to where AI is today, where it’s going, and the disruption you’ll face if you don’t adapt.
The bottom line: even the best pilots die quietly in a spreadsheet without executive buy-in.
The Lenses To Apply
Once leadership alignment is in place, the real question is how to evaluate platforms. It's less about feature checklists or the size of a connector library. It’s first about being clear on which capability you’re actually buying into. We landed on three core lenses:
1. Knowledge Retrieval: Building the Second Brain
This is where AI becomes transformative. Every business has years of knowledge scattered across Docs, Sheets, CRMs, Slack threads, proposals, and emails. Hunting through silos slows decisions and drains time. A strong knowledge retrieval platform acts like a second brain. Instead of just finding documents, it surfaces reasoning, history, and context. A new hire can instantly access decisions made years ago. A customer call can be informed by the full history of project work without digging. The shift isn’t necessarily about search (although critical), it’s more about synthesis. AI drafts, summarises, and enriches information, turning buried information into living context that actively supports the business.
2. Workflow Automation: Removing the Drudgery
The second lens is workflow automation, connecting systems and eliminating repetitive tasks that chew up hours. Think of sales teams that prep for meetings by pulling LinkedIn profiles, company info, and industry insights. That can now run end-to-end in the background, dropping a briefing pack in their inbox. Or ops teams drowning in reporting: automation consolidates data, generates summaries, and posts updates into Slack. Transcripts from customer meetings can update CRMs, notify colleagues, and draft follow-ups automatically. This isn’t flashy AI, it’s cutting drudgery so people can focus on higher-value work.
3. Agentification: Beyond Automation
The third lens is the frontier: agentification. Most businesses think they’re building agents, but really they’re stringing together "if-this-then-that" workflows with AI sprinkled in. Useful, but not true agents. An Automation Workflow is like a train on tracks: fast and reliable, but limited to the route you define. An Agent is like a car: given a destination, it chooses the best path, detours when blocked, and adapts as it goes. The trade-off is predictability vs flexibility. A true agent takes a goal, uses the tools and knowledge provided, and figures out the “how". Instead of “send an alert when the spreadsheet hits X”, an agent can monitor sales data, compare to history, flag anomalies, and suggest next steps. Platforms like Autohive are pushing this space forward where delegation replaces just automation, and digital colleagues emerge alongside human ones. This gets more into the detail than is necessary for most businesses, and for most, picturing Agents and Automations as one and the same is absolutely the right move for now.
No doubt some will find my workflow automation vs agent building definitions conflicting with their views. Once you've playing around these platforms, it become evident as to what true agency looks like with AI.
Solving for these problems isn't for everyone. And for us, knowledge retrieval, solving the repetitive task automation challenge, and building for an agentic future are all right up there with core requirements.
AI-Emergent vs AI-Native vs Obsolete
For most ANZ businesses, adopting ChatGPT, Gemini, or Claude will deliver quick productivity wins. That’s fine for the next 12–18 months, but those gains alone won’t shift the P&L in any meaningful way over the long-term. The real question is whether you want to be AI-emergent company, an AI-native company, or risk sliding into irrelevance and becoming obsolete.
AI-native companies are built with AI at the centre. If AI disappeared tomorrow, so would their business.
AI-emergent companies are established organisations that scale AI across core departments of the business supporting the business to run more efficiently. That’s where most companies will evolve to if they take this seriously.
Everyone else risks drifting into the obsolete bucket. And you don’t want to be in that category.
For more detail on these categories, check out Paul Roetzer's piece The Future of Business is AI, or Obsolete.
The Tipping Point for Overdose
At some stage, we had to move past tinkering with conversational AI tools and duct-taping point solutions together as this was never going to scale. Here's a window into our journey:
The first step was experimentation. Teams used Gemini as a co-pilot for daily tasks, and adoption spiked when Notebook LM arrived. Suddenly, grounding conversations in trusted internal data made AI genuinely useful and people could retrieve context instantly and accelerate their work.
The second step was automation. Pockets of the company started using tools like n8n and Relay.app. That’s when the velocity change became obvious: workflows ran ten times faster, repetitive processes vanished, and people were visibly freed up to focus on higher-value work.
That was the tipping point. We realised we couldn’t just rely on conversational tools stitched together with ad-hoc automations. If we wanted scale, we needed enterprise-level platforms, and tools that could automate repetitive tasks across the business, share agents between teams, and cut through the drudgery consistently.
More than that, it was about direction. At minimum, we need to build ourselves into an AI-emergent company. But the longer-term goal is clear: deliberately working towards being AI-native, where agents aren’t side-tools but digital colleagues that reshape how the business runs.
Reviewing Platforms for Procurement and Automation
When it comes to AI automation and agent platforms, the spectrum is wide. On one end you’ve got simple, general-purpose tools that plug into almost anything. On the other, coding-first environments like Cursor or Replit, where technical teams can build bespoke agents and applications from scratch. Most businesses will need a platform that sits somewhere in between. The key is knowing where a tool sits on that spectrum before you commit.
A couple of considerations framed our evaluation process on top of solving core business problems.
First, ease of use drives adoption. It doesn’t matter how powerful a platform is. If it needs deep coding knowledge or constant troubleshooting, it won’t scale across the business. Real change only happens when non-technical teams can build and manage automations themselves.
Second, adaptability matters in a market moving at breakneck speed. No one can say with confidence which platforms will still be leaders in 12–24 months. Long lock-in contracts are a risk. The smarter play is to procure in shorter cycles (12 months max), treat these tools as disposable if needed, and keep optionality open.
Third, connectivity makes or breaks the value. Most platforms cover the basics: Google Workspace, Microsoft Office, Salesforce, HubSpot, Slack. But gaps show up quickly once you get into ticketing systems, documentation managers, or e-signature tools. The depth of actions is just as important as the integration itself. For example, does a Slack connector only read messages, or can it also post, create channels, manage members, and close conversations? Does a Google Docs integration just retrieve files, or can it generate templates, insert placeholders, and update content dynamically? Those details decide whether automation actually sticks or just creates another silo.
To tie this back to business value, we ran a simple exercise: every department listed their repetitive tasks. For each task, we broke down time cost, data inputs, and failure points. That mapping became our blueprint, showing which tasks were ready for automation, which needed stronger data connectivity, and which might one day require true agentic systems.
The good news is once you learn one or two of these platforms, the concepts carry over. Triggers, actions, logic: every tool uses the same building blocks. That shared “language” makes adoption faster, because teams aren’t starting from scratch each time. Add to that the fact most platforms now give access to multiple AI models (OpenAI, Anthropic, Google, and increasingly Meta, and other open-source models), and you’ve got flexibility without vendor lock-in.
In short, our filter boiled down to three questions:
Can the business adopt it quickly?
Will it still be relevant in 12 months?
Does it connect deeply enough into our systems to matter?
Apply those, and classifying tools becomes much easier: automation & workflow-first (like n8n, Relay), agent-first (like Autohive), or enterprise-grade for automations, agents & knowledge retrieval (like Glean, Google AgentSpace).
n8n
Screenshot of the n8n workflow builder
For better or worse, n8n was where I cut my teeth on building AI automations. I’d never written a line of code before, but jumped in head-first, wiring up workflows for family and friends’ businesses. The learning curve was steep and I found myself screenshotting error messages into ChatGPT, Gemini, and Claude just to keep things moving. It was frustrating at times, but it forced me to understand how these tools actually work under the hood.
That grind turned out to be invaluable. n8n has been the backbone of a lot of what I’ve done with The AI Corner, automating the sourcing of news, jobs, and articles, keeping tabs on inboxes, and stitching together workflows that would have been a nightmare to manage manually. The power is there, no question. But the trade-off is brittleness. Workflows break often, sometimes for no obvious reason, and troubleshooting them can chew up hours. You need patience, and you need a bit of technical comfort.
n8n isn’t a complete “agent” platform in the way people like to talk about it. Most of what you’re building are automation workflows with AI sprinkled in, and agents build into various nodes (but the entire workflow isn't necessarily an AI agent itself). You can drop in nodes that think, analyse, or extract, but at its core it’s still “if-this-then-that” logic stitched together with some clever AI helpers. It’s incredibly flexible though as you can trigger workflows from scheduled jobs, webhooks, or APIs and other means, and then layer in branching, waits, loops, or custom code. Once you get the hang of it, you start to see how different AI nodes can be chained together to run on autopilot in the background of your business.
The reality is that adoption across a whole business would be tough. You’d need a couple of in-house specialists to really get the most out of it, because while the interface is visual, the concepts underneath still demand some knowledge of code. That’s why I see it as great for personal use cases, side projects, or SMB-level automation, but shaky when it comes to governance, security, and reliability at scale. On that basis, beyond a few teams leveraging n8n to build automations internally, we ruled it out for Overdose as a business as adoption would be siloed to a few minds and require a full-time Product Owner to manage the system.
Where n8n shines is as a prototype machine. You can turn an idea into a working flow quickly, test it, and see what value it creates. Where it struggles is in production: monitoring, stability, and long-term reliability aren’t its strong suit. It’s not a tool I’d hand to a client and say “this will just run". It needs oversight, redundancies, and regular fixes.
My view: n8n is a fantastic place to learn and to experiment. It gives you the flexibility to wire AI into almost anything and forces you to understand how triggers, actions, and nodes fit together. But if you’re trying to build enterprise-grade automation with the governance and stability you’d expect from critical infrastructure, it’s not the tool. For me, it’s been a force multiplier in learning and prototyping, but not something I’d bet the farm on in production.
Screenshot of the Relay.app Agent Builder
Relay.app sits firmly in the middle of the spectrum. Where n8n can feel brittle and technical, Relay is the opposite. It’s designed to be incredibly intuitive. The drag-and-drop WYSIWYG (what you see is what you get) interface is , and that makes it natural for almost anyone in the business to pick up and start building workflows. You don’t need to know JSON, APIs, or coding principles. If you can work your way around Google Workspace or Microsoft Office, you can figure out Relay. That’s why adoption happens so quickly: within a few hours, people are already creating automations that save them time. Relay has also quickly become a staple part of The AI Corner in the back-end due to its intuitive nature.
Some call these workflows “agents", but really they’re closer to "if-this-then-that" chains with AI steps layered in. Something triggers, an email arrives, a CRM entry is updated, a scheduled event fires, and the next domino in the workflow falls automatically. Relay is excellent for stitching together repetitive processes, creating micro-assistants, and handling the kind of drudgery that otherwise eats up hours. It’s not pretending to be a full agentic system that can figure out its own path, but that’s fine, it’s built to make automation accessible to many businesses and solopreneurs.
Relay.app really stood out here, offering the broadest set of out-of-the-box integrations and entry points we saw covering the bulk of today’s B2B SaaS stack. You can monitor app changes, schedule runs, hook into emails or webhooks and through other means, and use natural language to configure the steps. You also get flexibility in AI models baked in without having to manage your own API keys. And Relay shows you exactly how much AI credit a workflow is burning, which gives a layer of transparency you don’t often see in these platforms.
One of the most underrated features is the Relay Assistant. This isn’t just a chatbot bolted on, it genuinely helps you tweak and fix your workflows. Instead of just giving advice, it will actually apply changes you ask for, almost like having a built-in QA partner sitting inside the tool. That’s saved me more than once when a flow wasn’t behaving how I wanted.
The trade-off is flexibility. Relay is easier and faster to adopt than n8n, but it doesn’t offer the same depth. Those platforms let you work at the API and logic layer, giving you more control, but they also demand more technical comfort. Relay sacrifices some of that power to stay simple, which is why it’s so effective for SMBs or larger businesses that just want people to start automating without training up specialists.
The limitation for some businesses will be that Relay doesn’t do everything. It doesn’t natively handle knowledge retrieval or enterprise-level search the way Glean does, and it’s not aiming to build true agentic systems like Autohive. It’s strongest at stitching actions together across your apps and letting you automate faster than ever before. For a business that wants quick wins and broad adoption without needing engineering muscle, Relay is one of the best options out there. Jacob Bank (Founder of Relay.app) is also highly accessible, jumping on calls and responding to emails (not sure where he finds the time!), and is a prolific content creator with plenty of templates and guides shared to help people build in Relay. Big thanks to Jacob.
Screenshot of the Autohive Agent Builder
Autohive, based out of Wellington, is another genuinely exciting platforms in this space. Unlike Relay or n8n, its focus isn’t on stringing together if-this-then-that workflows. Autohive is built for true agents. Instead of defining a trigger and an action, you give an agent tools, knowledge, memory, context, and an objective, then let it figure out how to achieve the outcome.
What sets it apart is the combination of agent building and workflow orchestration. You can spin up standalone agents, or configure them to work together in a multi-agent, multiplayer-style mode. That unlocks far more complex use cases where agents share context, divide tasks, and build on each other’s outputs. Recently, Autohive has also rolled out a workflow builder that lets you schedule, monitor, and orchestrate agents at different times, which adds the robustness most businesses need to trust this tech in production.
The connector library has been expanding quickly, giving it solid reach into the most-used SaaS tools while also supporting reliable knowledge retrieval. Search performance across data sources is strong, and with GPT-5 now integrated as a model option, accuracy has stepped up noticeably, reducing hallucinations and drift (this is about the models, not the Autohive paltform itself). That’s a big deal when you’re asking agents to handle core business processes.
The other edge is its local advantage. Being NZ-based, the team is deeply invested in supporting the local community and customers. Having direct access to engineers and founders means you can shape the roadmap with them, escalate issues fast, and ensure your use cases are prioritised. That kind of partnership is rare in global SaaS and can be the difference between a platform that sort of works and one that really delivers. David ten Have, John-Daniel Trask, Hamish Taylor and Joe Sutheran are class to deal with and on the cutting edge of where these platforms are going.
In short, Autohive feels like it’s pushed past automation into genuine agentification. It’s intuitive to use, evolving rapidly, and backed by a team that’s building not just a product but an ecosystem. For any business serious about moving beyond incremental productivity gains and rethinking how work gets done, it deserves a spot on the shortlist.
Google Agentspace
Screenshot of the Google Agentspace Agent Builder
Google AgentSpace is where things start to blur between simple automation and true agent platforms. Because it’s natively integrated into Gemini and the wider Google Workspace, it already has strong foundations: document retrieval across Docs, Slides, and Sheets, plus the ability to work directly with Gmail and Calendar. For anyone already deep inside Google’s ecosystem, that integration is a huge head start.
At this stage though, AgentSpace is still in its early innings. It doesn’t yet have the same fully fleshed-out workflow builder or broad connector library you’d expect from a Relay.app or Zapier. Right now, it feels closer to an advanced Gemini “Gem”, with some agent-like capabilities layered on top, but not yet at the same level of orchestration. Its connectors and actions are still developing, which means flexibility is more limited compared to some of the other platforms we're trialling.
That said, the pace of development has been rapid. Even in the short time we’ve been piloting it, new functionality and integrations have been rolling out quickly. And if there’s one constant in tech, it’s that betting against Google is always risky. They have the resources, the research depth, the infrastructure (Vertex AI, BigQuery, the Agent Development Kit), and the distribution advantage of Workspace, which puts AgentSpace in front of millions of potential users overnight. Those ingredients mean gaps can close fast.
What’s interesting is the direction Google is signalling. They’re already positioning AgentSpace not just as an assistant, but as a hub where multiple agents can be designed, shared, and even work together across platforms. The release of protocols like Agent2Agent and the Agent Development Kit point towards a future where businesses won’t just use AgentSpace to retrieve or summarise information, but to orchestrate multi-agent workflows that connect to Salesforce, ServiceNow, or even competing ecosystems like Microsoft Copilot.
Right now, AgentSpace is best understood as a fast-evolving, ecosystem-native platform. It isn’t the most comprehensive tool yet, but for organisations heavily embedded in Google, it offers a very compelling future path. It’s less about where the tool is today and more about where it’s clearly heading: towards becoming one of the only credible competitors to OpenAI at enterprise scale, backed by Google’s enormous R&D engine and global reach. Backing the Google horse certainly isn't a bad idea for any business considering the innovation that's taken place in 2025 to date. Watch out.
Glean
Screenshot of Glean’s Agent Builder.
Glean sits firmly in the enterprise camp. It’s less about stringing workflows together and more about becoming the “second brain” across an organisation’s knowledge. Think of it as the smartest colleague in the room: the one who’s read every document, sat in every meeting, and knows who to ask when you’re stuck.
What sets Glean apart is the quality of its enterprise knowledge retrieval. This isn’t simple keyword search. Glean combines semantic and lexical search, building a knowledge graph of your company’s content, people, and relationships. It learns your acronyms, your projects, and the way your organisation works. The result is highly contextual answers, personalised to your role and respecting the permissions of your systems, ensuring you only see what you’re allowed to.
Beyond search, Glean is moving into agentification (arguably it sits in a grey area between automation workflows and agents in my view). Its Glean Assistant lives inside the tools you already use: Slack, Teams, Gmail, Zoom, ServiceNow, Zendesk, GitHub. And it can answer questions like “What’s the latest on Project X?” or “Who owns this account?” It doesn’t just retrieve, it summarises, drafts, and streamlines everyday tasks. Then there’s Glean Agents: configurable no-code or natural-language agents that don’t just provide answers, but take actions. They can trigger on events or schedules, route tasks, and integrate with enterprise platforms like ServiceNow and HubSpot. You can orchestrate multiple agents to handle more complex workflows, and there’s already a growing template library to jump-start common use cases.
But it’s not plug-and-play. Deploying Glean takes serious ramp-up, change management, and trust-building with all staff (this isn't a platform that can be rolled out to just a few users).
Glean is built for large organisations with sprawling digital footprints and governance requirements. But if your goal is enterprise-grade knowledge retrieval, combined with secure agentic workflows, it’s one of the strongest contenders out there.
In short, Glean is the heavyweight “enterprise AI work platform". Its strength lies in knowledge search that actually works, assistants that embed into the flow of work, and agents that can start taking action on your behalf. For organisations serious about cutting wasted hours at scale and giving people instant access to context, it’s not just another shiny AI toy, it’s the infrastructure layer for how knowledge and decisions flow.
Parting Thoughts...
These platforms are all moving targets. Features will change, connectors will deepen, vendors will rise and fall. What won’t change is the need for clarity: are you solving knowledge retrieval, automating workflows, or stepping into agentification? Anchor on that, and the noise fades. For us, the process wasn’t just about choosing tools, it was about charting our course: building ourselves into an AI-emergent company now, while laying the foundations to become AI-native. Because the risk isn’t choosing the wrong platform. The risk is doing nothing, and waking up obsolete.
Written by Mike ✌
Passionate about all things AI, emerging tech and start-ups, Mike is the Founder of The AI Corner.
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