The next generation of AI agents is shifting from passive workers that receive user commands and generate outputs to active agents that plan, act, observe, and improve on their own. Agents now choose how to complete a task, which tools to use, and whom (or which agent) to collaborate with. LLMs didn&rsquo;t invent agency, but they democratized it by turning frontier-level reasoning into a simple API call, letting teams compose complex systems from simple building blocks.
Going beyond the ad-hoc LLM API calls into agentic workloads requires an enterprise-grade platform. To provide the foundation for this shift, we built the Scale Generative Platform (SGP). We've found this approach requires two key disciplines: Agent Execution (which includes building and coordinating agents) and Agent Operations (which governs them).&nbsp;
Last week, <a href="https://scale.com/blog/agentex">we open-sourced the Agent Execution layer in SGP,</a> called Agentex. In this blog, we will explain how Agentex connects to the larger agentic infrastructure layer in SGP and discuss these concepts in detail.&nbsp;
<h2 dir="ltr">The Art of Agency</h2>
What does it truly mean for an AI agent to have agency? More than just executing a command, the core of agency lies the agentic loop. This entails a continuous cycle of planning a task, acting within an environment, observing the results, and reflecting on the outcome to improve the next cycle. This simple, powerful loop, an echo of the cybernetic principles from 70 years ago, is the engine of autonomy.
Posing the solution as an agentic loop gives us the opportunity to flip the traditional framing: instead of starting with a problem, we can start with this powerful solution and re-think what new problems we can solve. Today&rsquo;s agents are reactive, i.e. they wait to be called. With the loop, agents become proactive: they observe context on their own schedule, detect meaningful change, and act autonomously when it matters.
In practice, proactivity means an agent monitors signals continuously and acts when it detects a meaningful change.
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When a cold-chain shipment drifts out of range, the agent quarantines the lot, dispatches a replacement, and opens a QA ticket.
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When usage and NPS drop on a key account, it assembles a save play (exec check-in, fix plan) and books time on the CSM&rsquo;s calendar.
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When finance burn-rate predicts a budget overrun, it proposes rightsizing and reserved capacity, with one-click approval.
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When a schema drift appears in data pipelines, the agent quarantines the bad partition, rolls consumers back, and files a PR with lineage context.
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Each follows the same pattern: signal &rarr; significance test &rarr; action &rarr; guardrail &rarr; KPI, with autonomy tuned to risk (notify, propose, or act with rollback).
But scaling this concept from a single, clever chatbot to a robust global ecosystem of agents is a monumental challenge. Agents must be built with purpose, collaborate with precision, and operate with trust. This requires moving from theory to practice and establishing a new set of disciplines we call the "Art of Agency."&nbsp;
The Art of Agency is composed of two core practices:
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Agent Execution: Building resilient, autonomous agents (Agentic Engineering) and coordinating them in collaborative teams (Agentic Orchestration).
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Agentic Operations: Governing them safely and effectively at scale.
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<h2 dir="ltr">Agentic Execution: Engineering and Orchestration</h2>
<h3 dir="ltr">Engineering</h3>
To move beyond fragile, simple LLM calls to persistent, autonomous systems, design agents that are persistent, signal-driven, and adaptive:
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Persist and recover to manage long-term tasks. This is about resilience. We're moving to long-running agents that can work in the background, survive crashes, and pick up where they left off. This ensures an agent can persistently monitor a system or manage a project without constant human intervention and be resilient to external failures.
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React to signals and run concurrently to create a flexible, scalable system. Instead of agents directly ordering each other around in a rigid chain of command, they operate in a dynamic, decoupled way, often using an Event-Driven Architecture (EDA). In this model, one agent can publish an event (like "new sales lead acquired"), and other agents can react to it independently. This allows them to fire off a request and move on to other tasks&mdash;like analyzing a massive dataset&mdash;without bringing everything to a halt.
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Evaluate outcomes and adapt to improve performance over time. The most advanced agents don&rsquo;t just act&mdash;they react and reflect. They can detect changes in their environment (through events), adjust their strategy, and incorporate feedback from outcomes into the next iteration. For instance, an agent might maintain a rollout memory&mdash;a persistent, evolving record of its plan. This functions like a living draft, enabling the agent to update its approach in real time based on new observations or user input. In doing so, it can correct course mid-execution, continually refining its behavior. These adaptive feedback loops are what make an agent truly agentic.
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We&rsquo;ve packaged these principles in Agentex, a software framework we plan to open-source soon. As a key component of SGP, Agentex provides the engineering toolkit to build agents that can persist, react, and adapt, forming the building blocks for enterprise-scale orchestration.
<h3 dir="ltr">Orchestration</h3>
Once we have more than one agent, the real challenge becomes orchestration: coordinating specialized agents to achieve complex goals together. We&rsquo;re moving beyond simple, single-agent solutions to dynamic, multi-agent systems where tasks can be picked up asynchronously and executed collaboratively across a network of interoperable agents. Effective orchestration is what addresses the "conductor's challenge", turning a collection of autonomous agents into a cohesive, intelligent system.
To make this work at scale, three key capabilities are essential:
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Clear Governance and Roles: Every agent needs a defined scope, set of responsibilities, and clear "rules of engagement". When autonomous agents have overlapping objectives or conflicting priorities, these governance mechanisms ensure disagreements are resolved and accountability is maintained.
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Coordinated Workflows: High-level objectives must be broken down into smaller, interdependent sub-tasks that different agents can execute. Reliable orchestration systems manage this workflow, distribute work, track progress, and handle dependencies between agents. A key piece of this is transforming data so it's compatible for each agent, ensuring different agents can work together seamlessly.
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Dynamic Discovery and Communication: Agents must be able to find one another and their capabilities on-demand. Standardized protocols and registries allow agents to form ad hoc collaborations and build a fluid, intelligent network for cooperation.
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Each of these elements is a critical part of a larger architecture. Miss any one piece, and the entire workflow can collapse. This is why orchestration must begin with a foundation of reliable, durable agents; even the best conductor cannot compensate for unreliable building blocks.
<h2 dir="ltr">Agentic Operations: Safe, Effective Governance</h2>
As fleets of autonomous agents come online, we need a new operational paradigm, one that treats agents not as simple tools but as active digital participants within our ecosystems. Effective governance ensures these non-deterministic systems remain trustworthy, efficient, and aligned with organizational goals.
To achieve this, four disciplines are critical:
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Security and Access Control: Agents represent a new class of powerful insiders. Managing them requires a Zero Trust approach, with dynamic, task-specific identities and permissions that adapt in real time. As agents are expected to outnumber humans, traditional identity and access management models must evolve to handle this new scale and complexity.
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Accountability and Observability: Transparency is essential. Every agent&rsquo;s reasoning, actions, and outcomes should be traceable to provide visibility, enable debugging, and ensure compliance. Agent observability extends beyond system logs to capture the full &ldquo;chain of thought&rdquo; behind decisions and interactions.
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Operational Efficiency: Running fleets of large language models&ndash;driven agents can be costly. Sustainable operations demand real-time monitoring of compute, token usage, and API calls to maintain both performance and economic efficiency.
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Continuous Evaluation: Agents learn, adapt, and act autonomously, which makes continuous oversight indispensable. Traditional software testing is insufficient ; instead, we need dynamic evaluation methods, including automated techniques like an "LLM-as-a-judge", to confirm that agents continue to deliver reliable, aligned outcomes.
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Together, these practices form the foundation of AgentOps, a discipline focused on governing large-scale, autonomous systems safely and effectively. Our <a href="https://scale.com/genai-platform">enterprise platform</a>, SGP, is designed to operationalize this approach at scale.
<h2 dir="ltr">The Enterprise Imperative</h2>
Before a new marketplace for AI services can emerge, we need standards. Agents need common, reliable ways to find each other and discover the tools they can use. This standardization is what will allow a new "agentic web" to form, with agents as its fundamental building blocks. This process is analogous to how the API economy was built over the last decade. This is just the beginning though, and we&rsquo;re already in the process of leveling up what an agent does and how it behaves.
<h2 dir="ltr">Moving Beyond Generalist Chatbots</h2>
Sustainable enterprise value comes from specialised, proactive agents deeply integrated into the stack. Connected to ERPs, CRMs, HRMS and data planes, they can both access and act within systems of record, continuously sense context, run a significance test, and take the right action with the right guardrails, measured by clear KPIs.&nbsp;
For example:&nbsp;
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In hospitals, that means routing patients to the right care level and auto-scheduling follow-ups for high readmission risk.&nbsp;
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In retail, pre-empting stockouts and softening return spikes by adapting PDP copy or inventory flows in real time.&nbsp;
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Rather than dozens of point bots, organizations should build a reusable agent platform so policies, observability and components travel across use cases, with autonomy proportional to risk (notify &rarr; propose &rarr; act with rollback). Done right, these agents amplify teams and turn reactive workflows into continuous, accountable operations.
<h2 dir="ltr">The Strategic Roadmap for Adoption</h2>
Agentic AI is bigger than simple automation; it&rsquo;s an amplification layer for enterprise. The organizations that treat it as a tool to be constrained will realize incremental efficiency, and those that treat it as a new operating model will unlock entirely new forms of value creation. The path forward is less about control, and more about channeling agency toward impact. Here are key takeaways:
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Focus Deep Before Wide: Start where the impact is measurable. Prioritize structured, high-volume processes with clear ROI signals, places where rapid wins can validate both the business case and the model for scale. Early depth builds organizational confidence and sets the foundation for expansion.
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Design Autonomy for Impact: Autonomy should be tuned, not feared. In low-risk contexts, full automation can unlock speed and scale never before possible. In high-stakes domains, insert human oversight where it adds judgment and accountability. The goal is to apply agency intelligently so that value compounds safely.
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Platforms, Not Patchworks: The real leverage comes from shared infrastructure. A unified agentic platform allows teams to reuse components, standardize governance, and scale learnings across use cases. A platform mindset turns each deployment into a building block for the next, accelerating innovation rather than duplicating experimentation.
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Solve for Complexity, Not Avoid It: Enterprise constraints like security, integration, compliance are the proving grounds for competitive advantage. Address them head-on with clear frameworks for data privacy, bias mitigation, identity management, and change adoption. Building for scale from day one will future-proof success.
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Invest in Human Multipliers: A new kind of literacy is emerging: the ability to lead, supervise, and collaborate with teams of AI agents. The workforce will increasingly include &ldquo;Agentic Engineers&rdquo; who blend software, machine learning, and compliance expertise to design safe, productive ecosystems. Equipping teams for this shift turns every employee from operator to orchestrator.
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The true unlock of agentic AI, beyond lower cost, is higher ambition. Agents will power capabilities impossible for human-only teams: continuously adaptive supply chains, dynamic pricing engines, real-time risk management, and predictive operational control. These are entirely new revenue frontiers. The winners will succeed by building accountable ecosystems of agents that are durable, coordinated, and governed for impact. Start small, learn fast, and scale what multiplies value.&nbsp;
If you&rsquo;d like to chat with us about adopting AI agents with agency for your enterprise use case, please <a href="https://scale.com/demo?utm_source=blog&amp;utm_medium=blog&amp;utm_campaign=FY2511-ENT-WC-Agentex&amp;utm_content=launch-post">request to speak to the team here.</a> Learn more and <a href="https://scale.com/blog/agentex">try Agentex yourself here.</a>

What does it truly mean for an AI agent to have agency?