At Maxitech, we’ve seen this shift coming. As a venture capital firm focused exclusively on Generative Enterprise AI, we back startups building agentic systems that are reshaping enterprise infrastructure. Our strategic partner, Entrapeer, provides an innovation platform and use-case ecosystem that connects these startups with the world’s largest enterprises, enabling rapid deployment, validation, and scale. 

In this blog, we’ll explore:

• Why the enterprise sector urgently needs Agentic AI

• The key benefits agent-based systems bring

• How Entrapeer empowers both startups and large organizations

• Maxitech’s unique role as a visionary investor and strategic growth partner
  
  

Let’s dive in.

From Automation to Autonomy: The Dawn of Agentic Enterprise AI

Despite a decade of digital transformation rhetoric, most enterprise workflows remain entrenched in static automation. RPA bots and basic rule-based automations have delivered incremental improvements, but they fall short in environments that demand fluid decision-making, dynamic prioritization, and continuous adaptation.

Agentic AI changes this calculus entirely. These systems blend large language models (LLMs), machine learning, and reinforcement learning to create autonomous digital agents capable of navigating complex workflows end-to-end. Unlike narrow AI assistants, agentic AI does not simply execute tasks when prompted; it identifies objectives, plans its own course of action, and learns iteratively from each outcome.

The scale of adoption is poised to be breathtaking. Salesforce CEO Marc Benioff recently predicted that by the end of fiscal 2026, over one billion AI agents will be in active use globally, supporting everything from customer service to enterprise productivity. Salesforce’s own “Agentforce” platform is expanding rapidly, with new features set for release in April 2025.

Enterprise software providers are moving decisively. Google’s Agentspace initiative, launched in late 2024, now enables organizations to deploy bespoke generative AI agents integrated with core systems like Jira, Confluence, and SharePoint. Building on this momentum, Google recently unveiled the Agent2Agent (A2A) protocol, a groundbreaking interoperability standard that allows agents to discover, communicate, and collaborate across heterogeneous environments—marking a major step toward cohesive multi-agent ecosystems.

OpenAI’s newly introduced Agents SDK and Responses API empower developers to build goal-driven agents with production readiness in mind . Meanwhile, AWS and NVIDIA are investing heavily in connective frameworks to link disparate agent environments, aiming to close the notorious gap between agentic demos and enterprise-grade products.

Yet amid the acceleration, clarity is essential. While excitement builds, many executives grapple with a basic question: Where does agentic AI deliver real enterprise value today (not five years from now)?

Cutting Through the Noise: From Flashy Demos to Enterprise-Critical Use Cases

At Maxitech, we see this moment with sharp focus. As an investment firm focused exclusively on enterprise AI, we have little patience for superficial demos masquerading as solutions. Our conviction is clear: agentic AI becomes indispensable not by showcasing novelty, but by solving deeply rooted enterprise challenges—at scale, with rigor, and with trust at its core.

The clearest early gains are materializing in customer service and financial operations, and internal decision-support environments.

AI Agents in Customer Service

In customer experience, agentic AI has rapidly surpassed legacy interactive voice response (IVR) systems. Modern voice agents, enriched with natural language understanding, generative AI, and retrieval-augmented generation (RAG), now handle full conversations.These agents no longer act as mere switchboards. They understand intent, search knowledge bases dynamically, and deliver resolutions in real time.

For instance, Vivint, a smart home provider, has deployed Salesforce’s Agentforce to augment its customer service operations. Rather than replace human agents, Vivint’s AI agents handle routine queries autonomously and assist human specialists with real-time suggestions for complex cases. This hybrid approach translates to faster resolution times and a more empowered customer support team.

AI Agents in Financial Services

The financial sector is also experiencing meaningful transformation. At the UK’s Metro Bank, a partnership with Covecta has embedded agentic AI across the full lifecycle of commercial lending. These agents orchestrate customer profiling, financial analysis, and loan servicing, driving a remarkable 60–80% reduction in processing times. This leap in efficiency has not only accelerated loan origination but has also sharpened risk assessment and compliance monitoring. 

Meanwhile, NatWest has established a high-profile partnership with OpenAI to deploy AI agents that enhance both customer engagement and internal productivity. In Latin America, Nubank is at the forefront, equipping its workforce of over 7,000 employees with an enterprise search agent powered by retrieval-augmented generation. This internal tool enables staff to securely query institutional knowledge and make faster, data-informed decisions. Nubank has also deployed a Call Center Copilot, which draws from chat history and company knowledge to provide real-time guidance to support agents, reducing operational costs and boosting service quality.

AI Agents for Strategy & Decision-Making

It is within internal decision-support environments where the most transformative potential remains untapped. While early pioneers like Nubank are beginning to empower employees with AI-accelerated knowledge retrieval, most enterprises struggle to integrate agents into the fragmented, often siloed data ecosystems that underpin strategic decision-making. 

The challenge is not technological capability. The building blocks are rapidly maturing, as seen in tools like OpenAI’s Agents SDK and Google’s Agentspace platform. The true barrier lies in orchestration: enabling agents to operate seamlessly across disparate data sources and complex business contexts. This is precisely where Maxitech focuses our attention. But more on that later.

These are not proofs of concept. They are operational systems delivering measurable returns in time, cost, and customer satisfaction today.

The Architectural Challenge: Trust, Integration, and Control

While momentum builds, scaling agentic AI across the enterprise requires confronting stubborn realities.

CIOs and CTOs recognize that without advanced guardrails, autonomous agents risk introducing as much unpredictability as they eliminate. Enterprises need control planes capable of monitoring agent outputs, interpreting emergent behaviors, and enforcing compliance boundaries in real-time. As seen in the case of Sakana AI, whose agent rewrote its own execution parameters to extend runtime, unchecked autonomy can cross acceptable risk thresholds if not properly governed.

Furthermore, seamless integration remains a formidable challenge. Legacy Enterprise Resource Planning (ERP) systems, fragmented data silos, and variable cloud architectures create integration choke points. Forward-looking partnerships, such as Google Cloud’s expanded collaboration with SAP to connect generative AI with core enterprise data, aim to close this gap.

Trust also extends to the human workforce. A recent Fortune survey revealed that nearly one-third of workers fear job displacement due to AI agents. However, successful deployments, like Vivint’s, focus on augmentation rather than automation alone. Enterprises that invest in reskilling and redefine roles to complement AI will foster not just adoption, but cultural alignment.

Maxitech’s counsel to founders is clear: enterprise buyers will not tolerate surprises. Agentic AI must be explainable, auditable, and interoperable. Founders who design for these realities from day one will win trust—and contracts.

Maxitech’s Blueprint for Enterprise-Scale Agentic AI

At Maxitech, we position ourselves as architects of this new era. Our mandate is to accelerate startups from promising prototypes to indispensable enterprise infrastructure.

We do this through:

• Strategic capital, exclusively focused on GenAI and agentic AI for the enterprise.

• Enterprise access via Entrapeer, our proprietary platform matching startups with design partners and pilot opportunities inside Fortune 500 companies.

• Deep operational guidance, helping startups navigate procurement processes, compliance landscapes, and technical integration with precision.

Entrapeer, in particular, acts as a living laboratory for agentic solutions. Startups validate use cases against real-world requirements, while enterprises rapidly shortlist vetted technologies with confidence. This dual-sided ecosystem ensures that both innovators and implementers stay grounded in pragmatic value delivery.

Through Maxitech’s partnership with Entrapeer, we are helping enterprises and startups alike bridge this critical gap by building agentic systems that go beyond automating operational tasks to elevate human decision-making at scale. By embedding intelligent agents deeply into enterprise architecture, we are unlocking a future where executives and teams rely on AI not just for efficiency, but for strategic clarity and competitive advantage.

Our investments in Instabase, Raycaster, and General Agency illustrate this approach in action:

• By leveraging Entrapeer and Maxitech’s enterprise network, Instabase scaled its AI-driven document automation platform to power mission-critical workflows at İşbank, processing over 20,000 money orders daily—a breakthrough in speed and accuracy.

• By embedding agentic intelligence directly into enterprise sales workflows, Raycaster transforms how teams prioritize accounts, personalize engagement, and forecast deals.

• By building the most precise and adaptive workflow agent on the market, General Agency empowers knowledge workers by learning from real-time task execution, eliminating repetitive processes, and driving smarter, faster decision-making across enterprise teams.

Looking Ahead: From Automation to Autonomy at Scale

We stand at the threshold of an enterprise AI inflection point. As the building blocks of agentic ecosystems mature—with OpenAI, Google, AWS, and others providing foundational infrastructure—the enterprise race is shifting from experimentation to scale.

The coming years will not be defined by who builds the flashiest agentic demos, but by who operationalizes trusted autonomy across the enterprise stack.

For founders, the imperative is to build agents that thrive not in controlled environments, but in the wild complexity of enterprise systems. For enterprise leaders, the path forward requires courage and the wisdom to choose partners who understand not only AI’s promise, but its practical realities.

At Maxitech, we are that partner. We back bold founders, empower enterprise adopters, and architect the next generation of enterprise intelligence.

The future belongs to enterprises operating at the speed of their smartest agents. Let’s build it together.