5 different paths to launch AI agents (with examples)
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Everyone’s talking about AI agents. Some teams are quietly building. Others are still wondering where to start. And a few are already running huge parts of their orgs on agents.
How do you actually launch one that gets the job done?
This edition walks through five different paths to launch an AI agent.
Each path includes:
Who it’s best for.
What it automates.
Real examples.
It also outlines how each path maps to a broader AI adoption curve, inspired by Google’s maturity model. All together, the paths mapped against Google’s model looks like this:
Here’s a breakdown of each, with real examples.
PATH 1. The no-code starter
If you’re just getting started with AI agents, this is the lowest-lift and fastest path to real leverage. No engineers required.
This path is best for automating glue work: outbound, follow-ups, lead research, CRM updates. The kind of stuff early-stage teams often put on a SDR or virtual assistant.
Use this when you’re:
Wearing every hat.
Wanting results in hours, not weeks.
Lacking in-house engineering.
Testing outbound or reactivation as a channel.
Common mistakes to be aware of:
Prompt quality and targeting. Quality can dip fast if you don’t tune your prompts or tighten your ICP filters. And without CRM tracking, you won’t know what’s working.
What it looks like in practice:
This can take every form that you can essentially imagine. Here’s one example:
PATH 2. Wrap an agent around an existing workflow
If your team already has some structure (regular pipeline reviews, onboarding processes, forecast prep, etc.) agents can help you do the same work with fewer cycles.
This path works by wrapping a lightweight AI layer around something you already do consistently. It doesn’t require you to invent a new system, just automate the parts that drain time or block velocity.
Use this when you’re:
Drowning in recurring internal work.
Leading a team that follows process, but needs leverage.
Managing ops without a dedicated RevOps or CS ops hire.
Common mistakes to be aware of:
Assuming the agent can do the full workflow out of the gate. Start with narrow tasks (summarize → post → suggest), not complex decision-making. Human-in-the-loop QA is still key at this stage.
What it looks like in practice:
One example:
Pull Gong call transcripts → summarize key takeaways
Drop those into Notion or Slack with recommended follow-ups
Use LangChain or CrewAI to add logic (e.g. if churn risk detected → tag CSM + prep email)
Another example:
Agent build with Lindy.ai
This is our GTMnow podcast guest research AI agent. Every time we add a new guest to a spreadsheet, the AI agent jumps into action researching, creating a document, and attaching that document to the guest.
We still spend a minimum of three hours doing additional human research, including listening to past episodes, but it makes the process far more efficient.
PATH 3. Build a custom GPT copilot
If you're juggling things like hiring, pipeline recaps, creating investor or board updates and it all lives in your head (or in GSheets, Notion, etc.) a custom GPT copilot can be your silent, reliable teammate.
This path works best for repetitive communication tasks where the format is known, the content is semi-structured, and you want to stay inside your existing tools.
Use this if you’re:
A solo founder or lean GTM team who shares a lot of updates.
Tired of rewriting similar docs every week (updates, posts, memos).
Already working in Notion, Slack, or Airtable.
Common mistakes to be aware of:
GPTs are great at structure, but not nuance. If your tone matters (to investors, recruits, or customers), build a few reference samples into the prompt or system message. And always layer in human QA before hitting publish.
What it looks like in practice:
Here’s one example:
PATH 4. Embed agents in your GTM stack
When your customers are asking the same 20 questions every day or your team is buried in call notes and follow-ups, embedded agents can step in and take the first pass.
These agents live inside the tools you already use, such as Intercom, Gong, HelpScout, Zendesk. They respond to support questions, summarize calls, or even prep CRM updates – all without a human ever touching the task.
Use this if you’re:
Running a lean CS or support team and need coverage.
Struggling with follow-up lag after calls or demos.
Looking for leverage inside tools your team already lives in.
Common mistakes to be aware of:
Voice and fallback. These agents are your brand in moments that matter. Make sure the tone feels human, and that fallback paths (like escalating to a rep) are clear and fast. A bad support experience will impact your brand.
What it looks like in practice:
Here’s one example:
PATH 5. Build from scratch
This is the most powerful (and the most complex) way to launch an AI agent. You’ll need technical horsepower, but the upside is huge: full control, deep automation, and the ability to build something no off-the-shelf tool can match.
These custom agents can drive multi-step onboarding flows, real-time sales coaching, multi-agent task routing – truly whatever your stack and imagination allow.
Use this if you’re:
Technical (or have close access to engineers or AI talent).
Solving a high-value, high-complexity workflow.
Ready to invest in infrastructure for a long-term advantage.
Looking to build a proprietary GTM edge with AI at the core.
Common mistakes to be aware of:
Agent fragility and maintenance debt. These systems are powerful, but they break if the stack changes or the prompts aren’t updated regularly. Build with versioning, fallbacks, and human escalation paths baked in.
What it looks like in practice:
Here’s one example:
Jordan Crawford built an AI agent workflow that queries a Snowflake database of 172 million permits to find the top 3 most relevant ones for each prospect — data that’s independently valuable to them.
It costs just ~$0.30 per query, and it’s like having a personalized data science team for every lead. The flow works by:
Understanding the prospect’s business
Interpreting the structure of the database
Running multiple queries against the database until it found gold.
Surfacing the 3 most relevant permits or contractors based on that context
You can read or listen about this agent here.
These five agent paths can serve as a selection guide, but more importantly they represent stages in a broader AI maturity journey.
Google maps AI adoption across three (plus) levels:
Crawl – Start small with low-effort automation
Walk – Build consistency around structured workflows
Run – Integrate AI into core GTM systems
Master – Use AI to fully automate and orchestrate complex operations
This same progression applies to GTM teams adopting AI agents.
To bring this to life even more, here’s how a common GTM task evolves as your AI maturity increases: outbound follow-up.
Each stage adds more automation, context, and scale. This gives you more leverage.
Start where you are. Just automate one thing, then continue layering on from there. The leverage will compound.
“The worst thing teams can do is overthink it. Don’t spend 6 weeks on a spreadsheet. Just start. Pick a use case. Build an agent. Test it.”
– Ray Smith (VP of AI Agents, Microsoft)
For more helpful resources on AI agents:
GTM 139: AI Agents Are Changing Everything - Microsoft’s VP of AI Agents, Ray Smith goes deep on the new era of work and software.
Inside a marketing organization with no marketers, just 40 AI agents - an overview of the different agents built out across marketing.
Tag GTMnow so we can see your takeaways and help amplify them.
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