How We Use an AI Agent to Run Our Daily LinkedIn Engagement

When we launched ForgeLabs Consulting, we knew LinkedIn would be one of our most important channels. Our ideal clients are there: small business owners, solopreneurs, founders exploring AI, and professionals curious about what automation can do for them. The challenge was never finding the right people. It was showing up consistently enough to build real relationships with them.

We tried doing it manually for a while. It worked, but it was brutal. So we decided to practice what we preach and put an AI agent to work on our own LinkedIn engagement. Here is how we did it, what the actual workflow looks like, and what we have learned so far.

The Problem: Consistency Is the Hard Part

LinkedIn engagement is not complicated in theory. You scroll your feed, find posts that resonate, leave thoughtful comments, send connection requests to people in your space, and repeat. Do this every day and your network grows, your visibility increases, and conversations start happening.

In practice, it is the first thing that gets cut when client work picks up. On a busy day, you skip it entirely. Then one skipped day turns into a skipped week, and suddenly your pipeline has gone quiet.

The deeper issue was quality. After writing your twentieth comment of the week, everything starts sounding generic. "Great post!" and "Love this!" do not build relationships. Meaningful engagement requires actually reading the post, understanding the context, and adding something of value. That takes time, and when you are doing it alongside actual consulting work, the quality erodes fast.

We needed a system that could handle the repetitive parts of engagement while keeping the quality high and keeping us in control of what actually gets posted under our name.

The Tool: Abacus.ai DeepAgent

We did not build a custom automation stack. No workflow orchestration platforms, no custom scripts, no stitching together five different APIs. We use Abacus.ai DeepAgent, a commercial AI agent platform that can operate a browser, navigate LinkedIn, and execute engagement tasks with a level of sophistication that surprised us.

We chose it because it could do something most automation tools cannot: it actually reads and understands content. It does not just blast templated messages. It reads a post, understands the topic, and drafts a comment that is specific and substantive. That distinction matters enormously on a platform where people can instantly tell the difference between a thoughtful reply and a bot.

How a Typical Session Works

Here is what a real daily engagement session looks like with DeepAgent running the process.

Step 1: The Agent Scans and Likes

DeepAgent browses the LinkedIn feed looking for posts that match the topics and people relevant to our audience. It identifies 10 or more posts worth engaging with and likes them autonomously. This is the one action it takes without waiting for our approval, because a like carries low risk and high signal. It tells people we are paying attention without requiring us to craft a response.

Step 2: It Drafts Comments (But Does Not Post Them)

For each post it finds, DeepAgent writes a draft comment. These are not generic replies. The agent reads the full post, notes the author's role and background, and crafts a comment that references something specific about the content.

For example, when it found a post about someone passing a professional certification, the draft comment referenced the specific exam, congratulated them on their score, and asked a follow up question about what they planned to tackle next. When it found a post about a cybersecurity threat briefing, the comment engaged with the technical details and added a perspective on why the finding mattered for defenders.

Every comment follows a set of rules we defined upfront:

• One to three sentences with substance. No fluff, no empty praise.
• Only reference verified experience. The agent never fabricates credentials or claims expertise we do not have.
• No generic or empty comments. Every comment must add something to the conversation.
• Personalized with specific references to the author's background or the content of their post.

All of these drafts are delivered to us in a structured report, organized by author, topic, post URL, and the proposed comment text. Nothing gets posted until we review and approve it.

Step 3: It Drafts Connection Requests

Alongside comments, DeepAgent identifies 10 people per session who match our target audience criteria and drafts personalized connection messages for each one. Each draft includes the person's name, title, company, why they are relevant to our network, and a proposed message under 300 characters.

The messages reference something specific: a shared certification, a mutual connection, a role at a company we follow, or a piece of content they recently shared. These are not "I'd like to add you to my professional network" requests. They are short, specific, and give the person a reason to accept.

Step 4: We Review Everything (15 Minutes)

This is the part that makes it work. Every morning, we open the session report and review all the drafts. We approve most of them, edit a few, and skip the ones that do not feel right.

In a real session from March 12, the agent drafted 10 comments and 10 connection requests. We approved 7 of the 10 comments and skipped 3 that did not feel like the right fit. We approved 9 of 10 connection requests (one profile could not be found on LinkedIn). The whole review took about 15 minutes.

That approval step is not optional. It is what separates this from spam. We are not handing over our LinkedIn account and walking away. We are using the agent to do the research, the writing, and the organizing, then making the final call ourselves on what goes out.

Step 5: The Agent Executes and Logs

Once we approve or edit the drafts, the agent posts the comments, sends the connection requests, and logs everything. We get a completion report that shows exactly what was posted, what was sent, and what the status is on each action.

The logs include tracking for follow ups too. When connection requests are accepted, the agent has personalized follow up messages ready to send. When someone responds to a comment, we get flagged to jump into the conversation personally.

What the Output Actually Looks Like

To give you a concrete sense of the quality, here are a few real examples from our engagement sessions (names are from public LinkedIn posts).

When someone posted about passing a professional certification with a near perfect score, the agent drafted:

"Congrats on the 98%, that's an incredible score! The way it bridges attacker techniques with defender response strategies really changes how you think about incident handling. Welcome to the club!"

When a major tech company posted about their approach to AI and cybersecurity fundamentals, the agent drafted:

"The emphasis on fundamentals alongside AI is the right approach. Too many conversations jump straight to AI tooling without addressing whether the foundational detection and response capabilities are solid first."

When a fellow veteran posted about transitioning from military service to an IT career, the agent drafted:

"Fellow veteran here and this is spot on. The military teaches you how to learn fast, adapt, and perform under pressure. Those skills translate directly into IT and cybersecurity roles, even if the first job title doesn't look glamorous."

These are not perfect. We edit some of them. But they are remarkably close to what we would write ourselves, and they are generated in seconds rather than minutes.

The Guardrails We Set

Letting an AI agent interact with your professional network requires clear boundaries. Here are the rules we gave DeepAgent before it ever touched our LinkedIn account:

• Likes are autonomous; everything else requires approval. The agent can like posts freely, but comments and connection requests always go through human review first.
• No embellished experience. The agent only references certifications, skills, and accomplishments that are verified and real.
• No controversial or political content. The agent does not engage with posts that could create risk.
• Connection messages stay under 300 characters. Short, specific, and respectful of people's time.
• Comments are substantive. One to three sentences that actually contribute to the discussion. No empty praise.
• Volume stays moderate. About 10 comments and 10 connection requests per session, which keeps engagement rates high without triggering LinkedIn's automation detection.

What We Have Learned After Running This Daily

The 15 Minute Review Is Non Negotiable

The temptation is to just approve everything and move on. Do not do that. In every batch of 10 drafts, there are usually one or two that need editing and one or two that should be skipped entirely. The agent does not have perfect judgment about tone, timing, or whether a particular post is the right one to engage with. Your judgment fills that gap. That 15 minutes is what makes the difference between AI assisted engagement and spam.

Quality Over Volume, Every Time

We deliberately keep our daily volume moderate. Ten thoughtful comments will outperform a hundred generic ones. The math on LinkedIn rewards genuine interaction: comments that spark replies get algorithmic amplification. Empty comments get ignored. DeepAgent's ability to write substantive, specific responses is what makes this approach work at all. If the comments were generic, the whole system would fall apart regardless of volume.

The Agent Gets Better as You Train It

DeepAgent learns from the patterns of what you approve, edit, and reject. After the first week of sessions, the drafts started aligning more closely with our voice and preferences. By week three, we were approving 8 or 9 out of 10 comments without edits. The initial investment in reviewing carefully pays off in better drafts over time.

LinkedIn Free Tier Has Real Limitations

One thing we discovered: on LinkedIn's free tier, you cannot attach custom notes to connection requests beyond a certain daily limit. The agent drafts beautiful personalized messages, but sometimes they get sent as blank connection requests because the platform restricts the feature. We save those drafted messages as follow ups for when the connection is accepted. It is not ideal, but it works. If you are serious about outreach, LinkedIn Premium is worth the investment.

Structured Reports Change Everything

Before using DeepAgent, our LinkedIn engagement was untracked. We had no record of who we commented on, what we said, or which connections we had sent. Now every session produces a structured report: who we engaged with, what we said, what was approved, what was skipped, and what needs follow up. That paper trail is incredibly valuable for staying organized and following through on warm conversations.

The Time Math

Before the agent, a thorough LinkedIn engagement session took about 90 minutes to two hours per day. Finding the right posts, reading them, crafting thoughtful comments, researching potential connections, writing personalized requests, and tracking everything in a spreadsheet. On busy days, it just did not happen.

Now the agent handles the research, the writing, and the organization. Our daily involvement is about 15 minutes of review and approval. That frees up roughly 8 to 10 hours per week that goes straight back into client work, content creation, and actually having the conversations that LinkedIn engagement is supposed to generate.

More importantly, the consistency is dramatically better. We have not missed a single day since starting this system. The agent does not have busy days. It does not get tired. It does not skip the session because a deadline is looming. It shows up every day, and we show up for 15 minutes to make sure everything is right.

Why We Are Telling You This

We are an AI consulting firm. We help businesses automate their workflows, set up AI tools, and build systems that save time. It would be easy to make this story sound more impressive than it is, to claim we built some custom technical marvel from scratch.

The truth is simpler and, we think, more useful: we found a tool that does the job well, we set clear rules for how it operates, and we stay involved enough to maintain quality. That is the same approach we recommend to our clients. You do not always need a custom solution. Sometimes the right tool with the right guardrails is all it takes.

The core principle is straightforward: use AI to handle the repetitive, time consuming parts of engagement so you can focus on the conversations that actually matter. The agent finds the posts, drafts the comments, identifies the connections, and organizes the data. You make the decisions, add the human touch, and show up for the real conversations.

If you are spending hours on LinkedIn engagement every week, or worse, skipping it entirely because you do not have the time, this kind of system can change the equation. And you do not need to be technical to set it up. You need a clear picture of who you want to reach, what kind of engagement feels authentic to you, and 15 minutes a day to review the work.

Sources

1. Abacus.ai DeepAgent, the agent platform referenced throughout this case study. abacus.ai/deepagent
2. LinkedIn, official Help Center pages on connection-request limits and free-vs-Premium differences. linkedin.com/help
3. Internal session reports referenced (March 12, 2026 daily run, 10 comments and 10 connection requests reviewed): on file at ForgeLabs Consulting, available on request for prospective clients.