Project Claw 🦞: Field Notes from Agentic Work

But wordliftwe are building aye Agents for the work we already do.

This changes the relationship with technology.

We are not watching agents from a distance. We're not creating a demo for a conference and then imagining how people might use it later. we are putting paw Inside the daily rhythm of the team: in Slack threads, in reporting workflows, in client conversations, in SEO analysis, and in those moments where something breaks and needs to be fixed.

this is the story wordlift claw bot 🦞 (watch video Here To learn more: Our Slack-based agent for SEO, AI search, knowledge graph analysis, signal graph monitoring, and cause-effect reporting.

Can summon claw tools. It can recover data. It can use GraphQL. It can query the search console. It can generate reports. This may produce artifacts. This can explain the sources. It can compare variant and control groups.

But that's not the most interesting part.

What's interesting is what happens around the bot.

People raise questions on this. They fix it. They challenge its assumptions. They compare its output with previous manual work. They ask where the data comes from. They wait for it. They get disappointed when they fail. They encourage him to continue.

The agent isn't just automating the work.

It is changing the social nature of work.

Net

Over the past weeks I have been observing how the team uses wordlift claw bot In real slack threads.

Not polished signs.
Not idealized workflow.
Real work.

In one formula, an expert corrects the date of the intervention and provides the actual version and control lists. In the second, I ask the bot to use GraphQL to identify URLs where WordLift is present and use the Search Console to identify a cleaner control group.

After a few minutes, I ask the question that matters most:

Are we sure the control group is comparable to before the intervention?

That question changes the task. The bot is no longer just generating headline numbers. This must validate the causal design.

Then the pattern expands.

Someone asks what access the bot has. It becomes a new kind of onboarding: not a product journey, but a map of what Claw can actually see and do. Someone else calls it aligning marketing and sales by identifying low-cost fruits among organic and premium fruits. I call it analyzing a ranking entity page and moving on to editorial rewrites before queries, fan-outs, and competition.

This is when the agent begins to look less like a reporting assistant and more like an operating layer.

conclusion

The first thing we learned is that an agent becomes useful before it becomes completely reliable.

The second thing we learned is that adoption changes when the agent leaves the private workspace and enters the shared channel with the customer.

Inside the team, agents are allowed to be rude. It can fail, be re-run, and fixed. In the client-facing space, the bar changes. The output should be clear, down to earth and immediately useful.

That change matters.

This is the moment where An internal tool starts to become part of service delivery.

Client Trust is a Different Kind of Test

the first time i brought wordlift claw bot In a shared customer channel, the work was intentionally concrete.

I did not say this to make any big strategic claim. I asked it to check if the newly published Hub page is already getting traction from Search Console or Analytics.

That choice matters.

When you introduce an agent to the client-facing space, you don't start out with the most ambitious workflow. You start with a small, verifiable question.

The useful answer was not publicity.

The page had gone from zero impressions to initial visibility. The clicks were not there yet. Analytics showed a low number of engaged sessions. No AI referral traffic was visible yet.

Most importantly, the bot did not overclaim.

No AI referral traffic found.

That restraint is part of faith.

In a market where everyone wants to connect every activity to AI, agents became more reliable by separating what was visible in the search console and analytics from what was not yet visible in AI referrals.

Then, in a private conversation, the customer said:

It feels pretty solid.

That sentence matters.

This is not a testimonial. This is a threshold.

The agent had prepared something nice to be part of the customer's conversation. Not final enough to replace expert work. Not polished enough to become fully automated deliverable. But concrete enough to build confidence, move the conversation forward, and turn raw data into next action.

human layer

Each internal agent develops its own folklore.

Here folklore is made up of small lines.

What changed in the team

Before Clough, work moved from one tool to another. With Claw, work starts as a conversation and becomes executable.

The important change is not that humans disappear.

The change is that coordination, verification and production start happening in one place.

what changed in me

I just don't see using Teams WordLift Claw Bot 🦞. I'm also building it, shaping functions, testing workflows, and using it myself.

As a builder, I look at the system architecture.
As a user, I feel uncomfortable.
As a CEO, I see the deep implications of organizational patterns and digital transformation.
As an SEO, I care about functionality.
As a product person, I view every failure as a roadmap item.

And I realize the limits of trust when that person decides when to bring the agent into the customer-facing location.

That limit matters.

An internal bot may be experimental. It can be roughed up, improved and played again. Customer-facing agents have to earn their place. It earns it by being down-to-earth, transparent, and helpful in small moments before trusting big people.

Future

I would expect a similar agentic workflow in professional services.

Not complete autonomy.
No outright answer.
Not magic.

Instead: agents who initiate work, humans who define methodology, experts who validate assumptions, systems that uncover provenance, reports that can be regenerated, errors that become product feedback, and workflows that become reusable over time.

The agent will not replace the analyst.

This will change what the analyst spends time on.

The first draft took less time to assemble.
More time to verify designs.
Less time searching for data.
It would take more time to check whether the comparison is meaningful or not.
It takes less time to create charts manually.
It will take more time to decide what the chart proves.

and here it is paw Becomes much more than an internal experiment.

It becomes a way to structure the operating layer around SEO: queries, data sources, validation steps, client-facing reports, and repeatable workflows that typically only exist in people's minds.

final thoughts

The most important thing I'm learning from WordLift Claw Bot 🦞 It is not that AI agents can automate SEO tasks.

It is that they change the anthropology of work.

People start asking in different ways.
They start delegating in different ways.
They start challenging the output in a different way.
They begin coding their expertise into signals, fixes, and replays.

The agent becomes the mirror of the organization.

This indicates where the work is repetitive.
Where trust is fragile.
Where data provenance matters.
Where experts are necessary.
Where the product is still weak.
Where workflows must become the infrastructure.

This is why dirty Slack threads matter.

They are not just logs.

They are preliminary field notes of agentic work.

And, for us, they are also the blueprint for what we're building: Claw as an agentic operating layer for SEO teams.