What if running a company could be as programmable as playing a strategy game? Imagine an AI agent not just answering a question or making a prediction, but actually interacting with a business’s systems – analyzing data, sending recommendations, executing routine tasks – all within a controlled “sandbox” version of the enterprise. This is not science fiction; it’s the direction cutting-edge AI and automation are headed. At Thinktanq, a cornerstone of our approach is treating each business operation as a real-world environment that an AI agent can learn to navigate. In other words, we aim to turn proven playbooks into autonomous agents that can operate within the digital footprint of a company. Achieving this requires a rethinking of how we design processes and evaluations for AI, borrowing concepts from reinforcement learning and software engineering. Ultimately, building the autonomous enterprise means creating digital environments where AI agents are trained and trusted to carry out complex business tasks under human-defined guidelines.

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From Static Playbooks to Live Environments

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Traditional playbooks in private equity are static documents or checklists – essentially, distilled wisdom on how to achieve a certain outcome (reduce costs, integrate an add-on acquisition, etc.). They assume a human operator will execute them. Our goal is to elevate playbooks from static knowledge to interactive, simulated environments that AI can learn in. This is analogous to how AI researchers use simulated games or tasks to train agents. In a game like chess or Go, the rules are well-defined and an AI can play millions of rounds to learn winning strategies. Business processes are more complex and less deterministic, but we can still create sandbox versions of them for AI to practice.

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For example, consider the task of financial planning and analysis (FP&A) for a portfolio company. The “playbook” might involve steps like collecting actuals, comparing against budget, forecasting the next quarters, and generating a report with insights. We can instantiate this in an environment by connecting an AI agent to the company’s financial software (in a read-only mode for safety at first), giving it access to the relevant data (ledgers, ERP exports), and providing tools (like a spreadsheet API, or a report template) to act on. We then set objectives or rewards for the agent: e.g., minimize the forecast error, or identify 5 key variance explanations that match what an analyst would say. We might even simulate “questions” from an executive (“What happens if our cost of goods rises 10%?”) to see if the agent can handle them. In essence, we create a mini reality where the agent can attempt the FP&A process end-to-end. This approach aligns with the idea that the frontier of AI evaluation lies in building richer environments that mirror real-world workflows. Instead of testing an AI on a narrow metric (like accuracy on historical data), we test it on its ability to achieve meaningful outcomes in a realistic setting – did it produce a coherent, useful financial report? Did it surface the issues a human CFO would care about?

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One challenge is that business tasks are often long-horizon and involve multiple interdependent steps. An AI might need to perform a sequence of actions over hours or days, and coordinate with human inputs or other agents. This is far more complex than a single-step question-answer task. To address this, we draw on techniques from reinforcement learning (RL), which is specifically about training agents to achieve goals in environments over many steps. In RL terms, a company’s operations can be thought of as an environment, and things like quarterly results, customer satisfaction scores, or cost savings could serve as “rewards” or performance metrics. For instance, training an AI agent to assist in M&A deal execution might involve a sequence of tasks: analyze the virtual data room, draft diligence questions, populate an integration checklist, etc., with an ultimate reward if the deal closes smoothly and post-merger targets are met. As Mercor’s research highlighted, models need to be evaluated on longer-horizon tasks and collaborative environments – such as multi-party negotiations in an M&A deal or risk management over market cycles. We take this to heart by designing our AI agents to handle sequences of decisions and to operate with partial information, just as humans must in business situations.

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Creating Robust Evaluations (Evals) for Everything

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A critical piece of building autonomous enterprise agents is figuring out how to evaluate their performance in a meaningful way. Unlike games, where you either win or lose, business outcomes are multifaceted. How do we know an AI agent’s output is good enough? The answer lies in creating what we call evals – evaluation frameworks – for each task, which can often include both objective metrics and human judgment. One AI researcher aptly noted that the primary barrier to applying AI agents to the entire economy is building evals for everything . In other words, we need a way to quantify success for each business process we want to automate, otherwise we won’t know if the AI is truly doing a good job.

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At Thinktanq, when we turn a playbook into an AI agent, we simultaneously develop an evaluation rubric for it. Take the FP&A agent example: an eval might check that the agent’s financial forecast error is within, say, 5% of actuals (objective metric), and also that the variance explanations it provides are deemed reasonable by a human analyst (subjective metric). The latter might involve a human scoring the agent’s report on clarity and insightfulness, or even a secondary AI model trained to judge financial analyses. We are essentially encoding human expectations into a testable form. Some domains have clear-cut success criteria (for instance, an AI in charge of accounts payable can be evaluated by whether it paid all invoices on time with zero errors). Others are subjective, like an AI drafting an investment memo – here, we might use human experts to grade the memo or use proxy metrics like completeness of analysis.

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This approach echoes what Mercor’s “Era of Evals” manifesto described: evaluations that map real workspaces and deliverables are the new key to progress . By creating these evals, we turn squishy business tasks into something an AI can be trained against. It’s not easy – experts often hold different opinions on what a “good” outcome is in subjective domains . Our solution is to involve multiple experts to define rubrics and to allow some flexibility. For instance, we might have a rubric with several acceptable ways to frame a strategy recommendation, rather than a single “right” answer. This way, the AI agent isn’t narrowly trained to one person’s style, but rather to a broader standard of quality.

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Another important aspect is sim-to-real transfer: an AI that performs well in a simulated environment must also work in the messy real world. We mitigate this by making our training environments as realistic as possible – using real (but anonymized) data from companies, incorporating real-world constraints (like a simulated boss who might approve or reject the AI’s proposal), and even introducing noise or random events (e.g., “surprise, a new competitor entered the market – how does the plan change?”). The richer and more lifelike the environment, the more likely the AI agent will succeed when deployed live. There will always be a gap, but our philosophy is to push the boundary of that realism.

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Reinforcement Learning in the Enterprise

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The use of reinforcement learning (RL) techniques is a game-changer for autonomous business agents. In RL, an agent learns by taking actions and receiving feedback (rewards or penalties) on those actions. Over time, it “figures out” how to maximize reward by improving its strategy. In the context of enterprise tasks, designing the right reward function is paramount. We want the AI to learn the spirit of the task, not just hack a metric. For example, if we reward an agent purely for cutting costs, it might slash budgets in harmful ways. So we balance rewards: cut costs while maintaining quality and team morale (perhaps measured through a proxy like customer satisfaction or employee turnover).

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We often use intermediate rewards to guide the agent. As Mercor noted, intermediate rewards are critical, much like managers giving employees performance feedback before the final results are in . For a sales outreach agent, an intermediate reward could be set for each qualified lead generated, not just final sales closed. This ensures the agent doesn’t operate blindly for months but learns from smaller successes along the way. We also implement what we call rubric-based rewards for subjective tasks . If an agent is drafting an investment memo, we might reward it for including certain analysis sections (market, competition, financials) and citing data to back claims – these are structure proxies for quality. The real judge will be a human’s opinion on the memo, but by rewarding structural elements we guide the agent toward good practices while it hones the harder-to-measure aspects of persuasiveness.

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One practical example of enterprise RL we’re pursuing: an AI procurement agent. We give it the goal of saving money on a portfolio company’s vendor contracts. The environment is the company’s procurement system, and the agent can analyze past contracts, usage data, and even draft emails to vendors. We reward it for reducing cost per unit of value (so it doesn’t just cut necessary services), and penalize if a vendor relationship is damaged (we simulate this by tracking if the “vendor” agrees to a renewal or not – in testing, this can be another AI or a human evaluator playing the vendor). Through many iterations, the procurement agent learns strategies like timing negotiations at quarter-end, bundling purchases, or suggesting alternative suppliers. It effectively trains on a synthetic procurement negotiation game, so that when it’s unleashed for real, it has a playbook of tactics ready. Humans oversee all of this and step in for final approvals, especially early on. But as the agent proves itself, we can trust it with more autonomy.

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Bridging to the Real World

Ultimately, our autonomous agents must leave the sandbox and operate in live environments. We deploy them gradually. First, in shadow mode: the agent does the task in parallel to a human, but its outputs are not used, just compared. If it performs comparably to a human over a period of time, we move to augmented mode: the agent’s outputs are used with human review and sign-off. Many of our agents remain in this augmented mode permanently, because a human-in-the-loop is either required by regulation or simply prudent. For instance, an AI drafting legal documents might always need a lawyer’s approval, no matter how good it gets, due to liability reasons. That’s fine – even in augmented mode, the efficiency gains are enormous (the human spends 30 minutes reviewing instead of 5 hours drafting).

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In cases where it’s safe to do so, some agents reach fully autonomous mode. Think of an AI that manages a portfolio company’s cloud infrastructure scaling: it can automatically add or remove server capacity based on usage, following a policy it learned. Once we trust that policy, there’s no need for human intervention each time; it just runs. Fully autonomous enterprise functions are still few today, but we see them increasing steadily. Routine decisions in finance (approving small expenses), basic HR tasks (scheduling interviews, sending reminders), simple customer service responses – these are already being handled by AI at forward-thinking companies. The difference with our approach is the level of sophistication and learning ability we aim to imbue in the agents. They are not fixed automation scripts; they are adaptive agents that continue to learn from the environment. This means they can handle novel situations better and improve themselves over time, within bounds set by their training.

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One might ask, what about errors? What if the AI agent makes a bad call? This is why we invest so heavily in the evaluation harness around the agent. We build safeguards: for example, any action above a certain threshold of risk or cost must get human approval. Also, the agent’s decisions are explainable to the extent possible – it can show a reasoning trace or reference points (e.g., “I recommend this price cut because similar products saw increased volume that offset margin loss”). If an agent causes or narrowly misses a bad outcome in testing, we adjust the environment or reward function to catch that in training next time. In essence, we treat agent training as an iterative, never-ending process of refinement. As one LinkedIn observer summarized a similar approach: creating these evaluation environments and feedback loops is one of the largest build-outs we will undertake, but it’s what enables agents to learn effectively.

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The Path to Autonomy

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Building an autonomous enterprise is a journey, not a switch to flip. We are currently in an era where data and human expertise are the fuel for AI; to go fully autonomous, we must ensure the AI has access to the right experience and feedback to make decisions like a seasoned executive would . At Thinktanq, our path forward involves a few strategic steps:

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• Expand the Library of Environments: We continuously add to the types of business processes we can model and simulate. Today it might be FP&A and procurement; tomorrow, marketing campaign management, and after that, maybe strategic planning. Each new environment we build is an asset – it can be reused and adapted for multiple companies with similar processes. This is how we scale breadth.
• Enhance Data Integration: Autonomy thrives on data. We invest in connectors and pipelines to feed our agents real-time information securely from all relevant sources (financial systems, CRM, production databases, etc.). Many institutional investors are still stuck with spreadsheets and legacy systems that slow decision-making. By integrating data and making it machine-readable in our environments, we not only speed up AI learning but often help the company improve their data hygiene as a side benefit.
• Human Oversight and Training: We consider our pool of human experts as “coach teams” for the AI agents. Just as a sports team has coaches and play reviewers, we have people monitoring agent performance, analyzing their mistakes, and tweaking their training. This remains one of the highest leverage uses of human time – crafting the scenarios, rubrics, and guidance that shape AI behavior . We foresee that in the future, every company might have an “AI operations coach” role, akin to how every major factory has industrial engineers. For now, Thinktanq provides that as a service, effectively being the AI coach for our clients.
• Incremental Autonomy Releases: We incrementally dial up the autonomy of agents as trust is built. We publish clear reports to our stakeholders about how an agent is performing (e.g., our FP&A agent’s forecasts have been within 2% error for 6 quarters). These reports help build confidence among executive teams and investment committees that the AI can be relied on. We also address concerns like cybersecurity and compliance openly – any agent that touches sensitive data is thoroughly tested for security, and we log all its actions for audit purposes.

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The endgame we’re steering towards is a world where knowledge work converges on building and guiding AI agents rather than manually executing every task . In private equity, this means a firm could exponentially increase its operational effectiveness without linearly increasing headcount. A small team armed with a fleet of well-trained agents can oversee improvements in dozens of companies at once. An autonomous enterprise doesn’t mean a people-less enterprise; it means people focus on defining goals and standards (“what outcome do we want and why”), and agents handle the bulk of “how” to get there under those standards.

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We are mindful that “autonomous” doesn’t imply infallible. There will be failures and surprises – just as there are when humans are in charge. The difference is, when an AI agent fails, we can often diagnose exactly why by replaying the environment, and then correct the flaw for all future attempts. When a human makes an error in judgment, that lesson may or may not propagate to others. In this sense, autonomous systems, once matured, can contribute to a culture of continuous improvement that is very scalable. Each lesson learned by one agent in one context (say, how to handle a supply chain shock) can be transmitted to all agents in similar contexts across our portfolio.

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In summary, building the autonomous enterprise is about creating the digital playgrounds where AI can safely learn to do valuable work, and then gradually handing over the reins for specific tasks when the AI proves ready. It’s an ambitious undertaking – one that requires blending deep business expertise with state-of-the-art AI research. But the payoff is immense. Imagine the first PE firm that can say: we can simultaneously execute 100 value-creation initiatives across our portfolio, personalized to each company, with a lean central team – because our AI agents are working 24/7 alongside our people. That firm will have an unprecedented advantage in speed and scale. We are working hard to make Thinktanq’s clients be the ones who achieve it.