Growth Hacking Course
Agents and skills deployed into your commercial workflows - on platforms like Relevance.ai and Dust.tt, or custom-built - so your GTM systems run without adding headcount.
This is what it looks like when the platform arrives before the process.
The technology is the easy part. Getting agents from demo to production comes down to 3 design decisions:
You can't automate a process you haven't defined. Every deployment starts by mapping the workflow as it really runs - inputs, decisions, exceptions, owners - before an agent touches it.
Full autonomy is the wrong default. Agents fail multi-step tasks nearly 70% of the time in simulation testing. Override points and approval gates are designed in from the start.
Every agent gets a named owner, a definition of done, and an off switch. An agent without an accountable owner is unattended risk.
Working agents and skills, shipped into the tools your team already uses. Platform-agnostic: sometimes that's Relevance.ai or Dust.tt, sometimes your existing stack can do the job without a new subscription.
A map of your commercial workflows against what's worth automating: where hours go, where errors happen, and which processes are defined enough to hand to an agent.
Relevance.ai, Dust.tt, or the tools you already run. The workflow design and your security requirements decide the platform.
The reusable building blocks - prompts, tools, data connections - that make agents useful for your business specifically, built on your data and your standards.
Agents shipped into production with override points, approval gates and audit trails your compliance team can sign off on.
Your team runs it when I leave. Documentation, ownership, and the operating cadence for maintaining and extending what's deployed.
Hours returned and output shipped, tracked per agent. Agents that stop delivering get fixed or switched off.
The highest-value deployments in commercial teams are rarely the flashiest. These are the workflow categories I see returning the most hours:
Account research, contact enrichment and pre-call briefs assembled before your team asks for them.
When a qualifying signal fires, the agent drafts the outreach - your rep reviews and sends.
One source asset becomes the formats your channels need, in your voice, with a human edit before anything ships.
Records updated, duplicates flagged, fields filled from evidence - the unglamorous work that never stays done.
Pipeline movements, campaign performance and anomalies summarised and delivered each Monday, without anyone assembling them by hand.
Multi-step workflows - handoffs, follow-ups, approvals - run on schedule with humans at the decision points.
CEOs, CROs and CMOs at B2B SaaS and fintech companies with defined commercial workflows worth automating. If your processes aren't defined yet, start with the growth audit: it will tell you what to fix before you automate anything.
Agents are one layer of a larger system. They act on the signals the Signal-Based Engine captures and the priorities your ICP model sets - which is why I deploy them as part of the GTM Systems architecture.
A 30-minute conversation will tell you which of your workflows are ready for agents, which need defining first - and whether you need a platform at all.
Sometimes. Both are strong platforms and I deploy on them where they fit. The platform is the last decision in the design: the workflow determines the tool, and in some builds the stack you already run covers it.
That risk is why I design human-in-the-loop controls into every deployment: approval gates before anything external, override points at judgement calls, and audit trails for everything an agent does. Agents prepare and execute the mechanical steps, while people stay at the decision points that carry commercial or compliance risk.
A named person on your team, agreed before deployment. Ownership means someone reviews the agent's output, maintains its instructions as your business changes, and has the authority to switch it off. An agent without an owner drifts out of date, which is worse than no automation at all.
They compound. The Training Programme raises your team's fluency so they can work with and extend what's deployed. Agent deployment ships the systems themselves. Teams that do both end up owning their automation rather than depending on whoever built it.
With the workflow audit. It maps where your team's hours go and which processes are defined enough to automate safely. From there, the first deployment is whichever gives your team the most time back.