Agentic systems aren’t production-ready in PE, but SMBs are already catching up

No agentic system today matches the impact of ChatGPT’s GPT-3.5 release in Nov 2022 as a defining moment in tooling and impact. You can extend platforms like ChatGPT, Gemini, or Claude with assistants, reminders, and integrations, but true agentic autonomy remains further off.

Why agentic systems aren’t trivial to integrate today

Let’s start with some practical constraints and production experiments based on 2 years of LLM-augmentation in our DevriX labs + for several mid-market partners we optimize operations for. Here is where the model falls flat:

• UI-first tools like Lovable, Replit, and Bolt prioritize interfaces over deep automation logic.

• LangChain and CrewAI provide high-level patterns but provisioning and reliability remain rough.

• Multi-agent coordination tools fail under non-deterministic data flows and timeouts.

• OpenClaw is useful as a hobby project, but it is still insecure, opinionated, and complex in real deployment.

• Setting these systems up requires substantial engineering effort, limited error handling, and intricate access management across data sources.

• Deterministic workflow tools like Zapier, n8n, and Make still outperform most agentic deployments in reliability and predictability.

Between security, cloud credentials, API keys, OAuth token renewal, and multi-tool orchestration, true agentic autonomy for business execution remains at least 12 to 18 months away in mainstream use. Meanwhile, infrastructure work by providers (cloud, API routing, private networks) is still catching up.

This is not a knock on agentic AI. It is a reminder that tools without execution architecture create faster noise, not better decisions.

The real problem with most automation

Across 60+ mid-market portfolios we reviewed, a consistent pattern shows up:

• Nearly half of automation projects are accepted initially

• Most deliver little measurable execution impact

• Many increase complexity without improving outcomes

Traditional automation executes predefined tasks reliably. What it does not do is interpret goals, manage exceptions, or arbitrate conflicting conditions. This means:

• Execution risk remains hidden until late

• Cross-functional handoffs fail with lack of adoption and fear of change

• Decision quality does not improve measurably to justify incurred training and onboarding costs (and upcoming risks)

In other words, automation often accelarates the same execution issues your organization already has.

What agentic systems must actually deliver

For agentic systems to matter in business execution, they must go beyond sequencing steps. They have to:

1. Align to business outcomes - not task completion, but whether the execution actually delivered against the intended result

2. Monitor context and change - detect deviations from expected performance patterns, not just signal completion.

3. Escalate intelligently - wild alerts and noise bury operators. Prioritized, relevant exceptions are the key.

These capabilities are not widely available today at scale, even in tools that claim agentic autonomy.

I see the same pattern with 3 OpenClaw agents, 2 LangChain systems, and a few other prototypes, all acting as “assistants” or team members asking for input and reporting progess, confirming next steps, reviewing reports, or calibrating steps.

The way agents currently work is by fast-tracking research and generating ideas at scale. But humans always sit behind that journey. When a 1,500-words article no longer takes 6 hours, but 45 seconds, the editorial work rapidly goes up by a factor of 300.

A practical example: forecasting execution

In many portfolios, forecasting automation is set up like this:

• CRM syncs nightly

• Dashboards refresh hourly

• Weekly reports ship to executives

So far, this is just automation. It answers the question: Did the data load and refresh?

An agentic execution layer would instead:

• Compare actual performance signals to expected patterns

• Identify meaningful divergences early

• Suggest corrective actions

• Escalate issues based on impact on targets

This transforms the output from information to decision leverage.

What this means for private equity operators

Portfolios that use agentic systems as execution substrates rather than add-on tools capture real operational advantage:

• Higher decision velocity under uncertainty

• More resilient multi-function execution

• Greater predictability in value delivery

They do not just automate existing workflows. They surface failure earlier and reduce cognitive load on operators.

Failing to address this leads to more automation noise at scale, not better execution outcomes.

A simple operator playbook

Integrating agents and semi-deterministic automations is, however, an important unlock. We have similar integrations for 4 different private equity partners, and regularly speak with smaller scale SMEs introducing more automations across the board (thanks to less red tape and fewer risks for smaller-sized companies).

In your first integration journey with agentic systems, start with one execution surface, such as:

• Pipeline hygiene

• Forecast divergence monitoring

• Cash flow risk signal

Then:

1. Define what success looks like in that domain
   The outcome, not the tasks.

2. Map existing tools and identify where context and escalation logic are missing

3. Add an agentic layer that:

   • Detects divergence from expected patterns

   • Suggests corrective actions

   • Flags real risk

Frame this as execution governance improvement, not tool adoption.

True agentic execution is not about replacing operators. It is about amplifying their judgment in systemic ways.

Mario

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Working with me

🌐 Scaling $30M - $100M+ mid-market companies with value creation through RevOps, data engineering, and WordPress. DevriX provides full RevOps consulting + delivery with GTM enablement for PE-backed portfolio companies, traditional tech, healthcare, finance, and professional service businesses pacing toward revenue growth initiatives. Our standard retainers between $10K and $60K include revenue lifecycle services for marketing and sales leaders, FP&A for financial teams, pipeline enrichment through websites and dozens of lead sources, automations and delivery integrations, CRO and ongoing testing, product delivery and platform integration solutions, and more through our consulting solutions.

🚀 1:1 Consulting. At Growth Shuttle, I run two popular plans: Async Advisory ($3,500/mo) for $3M - $30M founders and executive teams and the smaller Strategic Growth Circle ($997/mo) for $100K - $500K entrepreneurs, agency founders, scale ups. My fractional executive plan is also available here.

📈 Building US LLCs from Europe. I help European and Asian founders scale faster through doola and their “Business in a Box” model. Also suitable for US citizens (given their bookkeeping solution), but in very high demand across Europe.

📊 Post-Merger Integration. I support M&A initiatives through Flippa’s marketplace. Working closely on PMI initiatives for PE companies and fast-growing startups integrating new companies within their portfolios, enabling data pipelines, and securing more deals through my personal network.