Agent Platforms
When we talk about deploying an agent into production, we can no longer just think about the prompts and tools it will use. We must consider an entire ecosystem of components that must accompany our agent at all times to guarantee the standards required by a production environment.
In this scenario, self-hosted architectures require a much greater magnitude of effort to meet all the necessary requirements, which consist of:
The reasoning loop
This is the heart of the agent. Given a high-level objective, it breaks it down into steps, decides which tools to invoke at each stage, and determines when the task is complete. In a custom deployment, the developer writes all the agent’s code using an open framework and is responsible for integrating all the pieces. Conversely, a managed platform offers several options, ranging from the ability to deploy our custom-developed agent (integrating more seamlessly with the components provided by the ecosystem) to using a pre-integrated harness. In the latter case, you only need to describe the objective, and the service itself handles the reasoning loop.
Short and long-term memory
Short-term memory maintains conversational context within a single session, while long-term memory allows the agent to remember the user and accumulate knowledge across sessions. Without long-term memory, an agent behaves as if it has amnesia—repeating questions and losing context every single time. In a custom environment, the developer is responsible for injecting the necessary tools (and external implementations) into the service tasked with managing the agent’s memory. On the other hand, an agent platform that offers this functionality natively will already provide a service meeting these requirements, handling both memory types and very likely allowing the configuration of different predefined strategies for long-term retention.
Tool orchestration
This is the mechanism through which the agent interacts with external systems—either to query information or to modify it. Throughout 2025 and 2026, an open standard has consolidated for this layer: the Model Context Protocol (MCP). MCP replaces proprietary wrappers with a common protocol: tools are published as MCP servers, and agents consume them through a unified interface.
Identity and authorization
An agent is not a person, but it acts on behalf of people or systems. It requires a clear mechanism for permission delegation, automated credential rotation, and a log of which effective identity executed each action. Flows like OAuth 2.1 with PKCE or on-behalf-of patterns have become the most commonly used strategies in this domain.
Observability
In an enterprise production environment, this aspect is non-negotiable; we want to know exactly what happened at any given moment—whether today, last week, last year, or even earlier. To achieve this, having a centralized system to query this information in detail (what happened during a session, which tools were called, etc.) is a mandatory requirement for compliance. Agent platforms generally offer unified ingestion mechanisms, compatibility with open standards (primarily OpenTelemetry), and the ability to send additional observability spans through modifications or wrappers.
Governance and policy
Conforme el ecosistema de agentes crece dentro de una organización, disponer de un mecanismo centralizado que permita gestionar qué agentes pueden interactuar con qué herramientas, bajo qué condiciones y con qué restricciones de contenido se convierte en una necesidad crítica. Esta capa abarca desde filtros de contenido dañino hasta límites de uso, restricciones de acceso a herramientas y reglas de delegación en arquitecturas multi-agente. Un único permiso mal configurado puede propagarse en cadena por toda una jerarquía de agentes, por lo que la política debe aplicarse de forma centralizada y consistente, no agente a agente. En un entorno a medida, el desarrollador es responsable de implementar y aplicar cada guardrail individualmente. En cambio, las plataformas de agentes gestionadas ofrecen mecanismos configurables que se aplican de forma uniforme a todos los agentes del ecosistema: filtrado de contenido, control de acceso a herramientas y políticas de delegación entre agentes.
Registry
As the number of agents, tools, and MCP servers within an organization grows, a wild proliferation of resources can emerge without any central visibility or control. A registry is a centralized, governed catalog where all ecosystem assets are published, versioned, and discovered: agents, MCP servers (generated from OpenAPI specs, existing endpoint URLs, or published directly), individual tools, and custom resources. It allows teams to find what already exists before building something new, manage the lifecycle of each resource, and ensure that only approved assets are accessible in production. In a custom environment, this catalog must be built from scratch. Managed agent platforms provide a registry service featuring semantic search, approval workflows, and access control, treating all these resources as auditable enterprise assets rather than scattered artifacts.
¿Vas a construir toda esta infraestructura a medida desde cero o prefieres centrarte en el valor real de tus agentes de IA?
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