What if AI autonomously handled software development? Agentic AI systems, like GitHub Copilot Workspace, already aim to take high-level requests and autonomously write, test, and debug code. This leap beyond generating snippets to automating end-to-end cognitive workflows reshapes knowledge work and software interaction. Understanding Agentic AI’s impact on strategy, roadmaps, and workforce is now imperative for leaders. According to respondents of a recent Boston Consulting Group survey, una de cada tres empresas a nivel mundial tiene previsto destinar más de 25 millones de dólares a la IA en 2025.
Definición de la IA agencial: más allá de los chatbots, hacia los agentes autónomos
Forget simple chatbots. Agentic AI represents systems designed to autonomously pursue goals. Given a high-level objective, an agentic system can devise a plan, execute steps by interacting with various digital tools (APIs, browsers, applications), and adapt based on outcomes. It’s the transition from AI that responds based on patterns to AI that acts to accomplish specific tasks.
Much confusion stems from the fact that agentic AI often leverages powerful Gen AI models like OpenAI’s GPT-4o or Google’s Gemini as its core reasoning engine. However, the agentic framework – exemplified by platforms emerging from OpenAI’s Assistants API or open source toolkits – provides the crucial layers of autonomy, planning, memory, and tool interaction. Imagine that instead of having a powerful engine, you have a self-driving vehicle that uses that engine to navigate traffic, follow routes, and reach a destination. This dynamic capability separates it sharply from brittle, script-following robotic process automation.
IA agencial frente a IA generativa: el avance práctico que está impulsando el revuelo mediático
Generative AI’s power to create content – from code snippets via the original GitHub Copilot to marketing copy via Jasper – is undeniable. However, agentic AI harnesses this generative power and makes it actionable. It’s the difference between asking an AI to write Python code vs. asking an agent to build, test, and debug a functional Python script that integrates with a specific API to achieve a business outcome.
The current wave of hype, evident in soaring valuations for AI startups and intense focus from tech giants, stems from this leap to autonomous action. While Gen AI might draft sections of a business plan, an agentic system could be tasked with “Analyze market trends for Product X, identify key competitors, draft a competitive strategy document, and schedule a review meeting,” orchestrating research, analysis, generation, and scheduling tools. This potential for automating complex, multi-step workflows end to end, moving beyond human-in-the-loop for every step, fuels the immense strategic interest and investment.
El imperativo agencial: redefinir el software, desafiar a los líderes del mercado
Why is virtually every software company, from hyperscalers to SaaS providers, racing to develop an agentic strategy? Because it promises to redefine user interaction and software value. As Microsoft CEO Satya Nadella said, technologies like Copilot “fundamentally change how we relate to computing,” acting as orchestrators.
Vemos cómo esto se está desarrollando rápidamente:
- Microsoft’s Copilot Ecosystem: The broad rollout of Microsoft 365 Copilot across Word, Excel, and Teams isn’t just about embedding generative features; it’s about enabling users to delegate tasks like “Summarize unread emails from Project Phoenix and flag action items” or “Analyze Q4 sales data in this Excel sheet and create a PowerPoint summary.” The recent launch of GitHub Copilot Workspace explicitly targets this, aiming to take developer requests from issue description to tested pull request with minimal intervention.
- Google’s Agentic Integrations: Google continues to weave Gemini more deeply into Workspace, enabling complex cross-app actions. The evolution of AI Overviews in Search hints at a future where Search doesn’t just provide links but performs actions or complex research tasks directly.
- Salesforce, ServiceNow, Adobe: These platforms are integrating agentic features (like Salesforce Einstein Copilot or ServiceNow’s Now Assist) to automate complex CRM, IT service management, and creative workflows, allowing users to interact via natural language goals rather than complex UIs.
- Startups: Companies like Adept AI, focused on teaching AI to use existing software interfaces, underscore the ambition to create universal agents, attracting significant venture capital interest throughout 2024.
This trend threatens to disrupt established software models. If users can achieve complex outcomes through conversational agents layered on top of traditional applications, the underlying application’s UI and feature set become less critical. Incumbents must adapt or risk being abstracted away.
Reformular la protección de datos: hacia una ciberresiliencia autónoma
El sector de la protección de datos, que se enfrenta a amenazas cibernéticas cada vez más graves, como los sofisticados ataques de ransomware en varias fases que se han ido observando con mayor frecuencia a finales de 2024, necesita urgentemente las capacidades proactivas que promete la IA agentiva:
- Proactive defense beyond anomaly detection: Existing AI flags anomalies, but agentic systems could go further. Imagine an agent detecting ransomware reconnaissance patterns, autonomously snapshotting critical VMs via the backup platform, verifying snapshot integrity, and isolating suspicious endpoints before encryption begins – drastically reducing blast radius.
- Intelligent, context-aware recovery: Instead of rigid recovery plans, an agent could analyze an attack’s scope (e.g., using threat intelligence feeds via API), identify the safest recovery points across multiple systems, orchestrate the restore using the backup infrastructure, perform automated data validation checks, and even generate post-incident reports.
- Dynamic policy optimization: Agents could analyze evolving compliance mandates (like updated GDPR or CCPA rulings) or observed protection gaps and automatically adjust backup policies, frequencies, and retention settings, maintaining continuous alignment without manual overhead.
Commvault, Arlie y el futuro: estructuras agenticas para la resiliencia de los datos
Navigating this evolution requires a data resilience platform built for an AI-driven world. At Commvault, our AI copilot, Arlie, already leverages AI to provide insights and streamline operations. But our vision extends further, toward enabling Agentic Fabrics.
This concept envisions an intelligent, interconnected architecture where AI agents, guided by Arlie, operate and coordinate autonomously across your hybrid data landscape. This isn’t just about one assistant; it’s about a network of specialized agents working together:
- Un agente encargado de supervisar el estado de seguridad podría detectar una amenaza y avisar a otro agente a través de Arlie para que proteja al instante las copias de seguridad pertinentes.
- Following user intent (“Recover the CRM database to its state before yesterday’s suspicious activity”), Arlie could direct agents to orchestrate the complex restore across application servers, databases, and cloud storage, confirming data integrity post-recovery.
- Los agentes podrían identificar de forma proactiva oportunidades de ahorro de costes analizando los patrones de uso de datos y migrando automáticamente los datos inactivos a niveles de almacenamiento más económicos, en función de los objetivos establecidos en las políticas.
The Agentic Fabric represents the next evolution in intelligent architecture – a coordinated ecosystem where AI agents operate autonomously yet collaboratively across your entire data landscape. This isn’t merely about deploying isolated assistants; it’s about creating an interconnected network of specialized agents that communicate and coordinate seamlessly:
- Los agentes de seguridad pueden detectar amenazas al instante y activar medidas de protección a través de otros agentes especializados sin intervención humana.
- Recovery processes can follow natural language intent (“Restore our systems to before the breach”) while orchestrating complex technical workflows across hybrid environments.
- La optimización de los recursos se convierte en un proceso continuo, ya que los agentes identifican de forma proactiva las oportunidades de mejora y las aplican de acuerdo con las políticas definidas por la empresa.
This shift toward Agentic AI demands organizations reconsider three critical dimensions: their data architecture (enabling it to support autonomous yet secure agent operations), their governance frameworks (establishing appropriate guardrails for agent actions), and their resilience strategies (leveraging agents to anticipate rather than merely respond to threats). Organizations that embrace this paradigm won’t simply gain incremental efficiencies – they’ll fundamentally transform how they protect, manage, and leverage their data assets.
As the digital landscape grows increasingly complex and threat vectors multiply exponentially, the question becomes unavoidable: Is your organization preparing for a world where AI doesn’t just assist your team but actively safeguards your most valuable assets? The agentic revolution isn’t approaching – it’s already reshaping enterprise technology. Are you ready to evolve with it?
