Get your AI agent in front of the developers, ops leaders, and reporters who decide what gets shipped.
Agent companies do not lose because the agent is bad. They lose because three competitors get covered first and two creators ship demos that look better. Influencer marketing and PR fix that.
The State of AI Agents Marketing
Agents are one of the most crowded categories in AI right now. There are coding agents, browser agents, voice agents, sales agents, customer-support agents, ops agents, and a long tail of "agentic" everything. Buyers are exhausted, reporters are jaded, and "we built an agent that does X" stopped being a story sometime in 2024.
Marketing an agent company in 2027 means proving three things in public: the agent actually completes a real task end-to-end, it does it reliably enough for production, and the team behind it understands the failure modes. Polished launches and strong narratives still matter. The work is making sure they are grounded in real proof so engineering, ops, and executive buyers can all say yes.
What Most Agencies Miss
These are the issues that come up every time we plan a campaign in this vertical, regardless of company stage.
Half the agent demos on X are cherry-picked or running on a perfect prompt. Buyers have learned to discount everything they see. Your launch needs proof: replays, side-by-side comparisons with humans, and ideally a third party who actually used it on their own workflow.
An agent gets evaluated by a senior engineer or platform lead even if a non-technical exec writes the check. That means your marketing has to satisfy two audiences: the engineer reading the docs, and the exec reading the case study. Most agent companies pick one and lose the other.
Reporters covering agents in 2027 are looking for more than capability moments. They are publishing pieces on hallucination rates, error recovery, observability, and the gap between demo and prod. Pitches that pair a clear capability story with a credible reliability angle land far more often than capability alone.
Three companies in your sub-category are using the same base model with similar scaffolding. Whoever ships the most credible demos and wins the most creator integrations becomes the default, and stays the default through the next funding cycle.
Who Actually Buys
Who signs the check, who has veto power, what they care about, and what kills the deal.
Decision maker
The person who signs off
Usually a VP of Engineering, head of platform, or a technical founder/CTO at companies between 50 and 2,000 employees. At enterprise, it shifts to a director of automation or AI platform lead. The economic buyer above them rarely overrides a strong engineering recommendation.
Who else gets a vote
Senior engineers on the team that will actually deploy the agent, security and compliance reviewers, and at least one skeptic who has seen a previous AI tool fail in production. The skeptic is the most important person in the room. Win them and the rest fall into place.
What they care about
Reliability metrics, observability and replay tooling, latency, total cost per task, model-vendor flexibility, security posture (SOC 2, data handling, on-prem options), and how the agent fails. They want a demo that includes a failure case and a clear story for what happens next.
What kills a deal
Vague pricing, lock-in to a single model provider, no replay or audit trail, marketing that overpromises capabilities the team has to walk back in the eval call, and any whiff of "we built this in three weekends." Buying committees in this category have very low tolerance for inflated claims right now, so the launch story needs to match what the eval actually shows.
What We Do
Run one campaign or stack them around a launch. Every engagement is scoped to the moment you are building toward, with no wasted budget. Click into any service to see exactly how we run it.
Walkthroughs, reviews, and reaction content from technical creators who already reach AI agent buyers. We source, brief, contract, and report.
Your announcement seeded across a vetted network of X creators inside one coordinated window, at a CPM you can plan around and reach guaranteed in writing.
Scripted, storyboarded, and produced in house, engineered for retention, then amplified by the creators who already reach AI agent buyers.
A network of 500+ creators and social accounts making their own content about your product, in their own voice. Live in 3 to 6 weeks.
Channel Mix
Influencer marketing is what we lead with, and many engagements run creators alone: seeding a launch video, amplifying an announcement across X. When an engagement also covers PR, this is the split we typically use for AI agent companies.
Influencer
55%
PR
45%
Influencer
Independent creators on YouTube and X are a primary discovery surface for the engineers, ops leads, and platform decision-makers evaluating agents. A strong build-along from a respected creator gives buyers a "I trust how this looks in real hands" moment that complements the rest of the launch.
PR
Reporter coverage at TechCrunch, The Information, and VentureBeat is the single biggest credibility unlock in this category. One feature in The Information legitimizes you with the buyer who is comparing eight options.
Creator Archetypes
Each archetype converts a different stage of the buying journey. We build the campaign mix from the ones that fit your stage and ICP.
YouTube
Long-form (8 to 20 minute) videos walking through how they built or evaluated an agent on a real task. Audience is engineers and indie hackers actively comparing tools.
How we use them
Build-along sponsorships where the creator uses your agent to solve a problem they care about. Conversion holds up best when the problem is real and the demo is unedited.
Podcast
Hosts running a weekly or biweekly podcast on practical AI engineering. They book founders, researchers, and platform leads who have shipped real agents.
How we use them
Founder or technical co-founder interview as part of a broader narrative arc, usually paired with a launch or eval study.
X
Engineers, infra leads, and platform builders who post real failure modes, eval results, and side-by-side comparisons. Smaller follower counts than mainstream AI X but higher buyer density per follower.
How we use them
Paid evaluation or "first impressions" thread tied to a launch. Most effective when the operator has no existing relationship with you, because buyers treat them as honest brokers.
Director-level operators in ops, RevOps, or customer-experience writing about how their team uses agents. Audience is the non-technical exec who signs the check.
How we use them
Sponsored case study posts or a paid newsletter feature where the creator interviews one of your customers. Slower-converting but moves enterprise pipeline.
Press Targets
Creator campaigns are what we lead with. When a moment also warrants coverage, these are the publications and the specific beats we pitch into. We do not mass-blast. Every angle is built for a named reporter.
Tier 1 priorities
TechCrunch
AI / agents desk
The default outlet for funding announcements, agent launches, and category narrative pieces. Their AI desk has multiple reporters covering the agent ecosystem, and a feature here anchors the rest of the launch wave.
The Information
AI / enterprise software
Sets the agenda for what enterprise buyers consider a serious agent vendor. A feature here legitimizes the company inside Fortune 500 eval cycles and the venture community at the same time.
Ars Technica
AI / autonomous systems
Long-form technical narrative coverage that engineers and informed enthusiasts trust. Strong for reliability, safety, and "how the agent actually works" pieces that survive scrutiny on launch day.
Also placing in
VentureBeat
AI infrastructure
Strong for technical depth pieces around evaluation, reliability, and agent benchmarks. Their AI vertical is read by platform engineers and infrastructure buyers, not just investors.
Forbes
AI 50 / enterprise AI
Lists and ecosystem coverage carry weight with the non-technical executive who has to sign off on a new agent vendor. The AI 50 list specifically accelerates enterprise deals.
Bloomberg
Enterprise AI / business tech
Reaches the buyer-above-the-buyer and the institutional investor audience. Useful for funding rounds, named partnerships, and category-defining adoption stories.
Practical AI
AI engineering podcast
Long-running practical AI engineering podcast. Reaches working ML engineers, platform leads, and the technical buyers who actually deploy agents in production.
Stratechery
Strategic tech analysis
Strategic analysis read by founders, executives, and investors. A mention here shapes how the agent category is understood at the C-suite and board level.
Story Angles That Work
Story shapes that tend to land in this vertical. Use them as a starting point. Every campaign gets a custom angle built around your actual proof.
"We ran our agent on the same 500 real-world tasks as a human team for six weeks. Here is what broke and how we fixed it."
Why it works. Reporters in this category are looking for evals on real work, not toy benchmarks. Honest failure data tends to earn coverage.
"The browser agents collectively crossed a meaningful task-completion milestone this quarter. Here is what they got right and where they still fail."
Why it works. Ecosystem stories that include your numbers but read as a category piece earn coverage from outlets that would skip a single-vendor pitch.
"We open-sourced the eval harness we use internally to ship agent updates."
Why it works. Sharing the tooling earns goodwill across the engineering community and gives buyers a low-friction first interaction with your product.
Funding round narrative: "Why a tier-1 fund led the round despite betting against agents in 2024."
Why it works. Funding stories that include a credible reversal are more interesting than funding stories alone, which forces a reporter to write a real piece instead of a roundup.
Common Pitfalls
Avoid these and you are already ahead of most of the field.
Launching only with a polished demo, without supporting proof.
Pair the launch with a public eval (even a small one), a methodology note, and a few real workflow examples. The full launch package, narrative, demo, and evidence, is what gets shared inside engineering, ops, and exec teams alike.
Sending the same announcement copy to TechCrunch, The Information, and Bloomberg.
Each outlet wants a different angle. Lead with funding or momentum at TechCrunch, with enterprise traction at The Information, and with category implications at Bloomberg. Same facts, different stories.
Briefing creators only on a high-level product walkthrough.
Pair the polished walkthrough with a real-use moment, the creator running the agent on their own workflow, including the friction. The combination converts harder than either alone.
Leading press and creator briefs with capability claims alone.
Pair every capability claim with a reliability story: how often the agent succeeds on a defined task, where it fails, and what happens when it does. Reporters and buyers both engage more with stories that include both.
FAQ
Asked by founders, marketing leads, and operators in this vertical every week.
Book a free strategy call. We will walk through where you are in the launch arc, the publications and creators we would prioritize for your stage, and how the engagement would look.
