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The Agentic Architecture: From Apps to Services

The web is undergoing its fourth major architectural transformation. After connecting documents, then people, then apps, we’re now connecting agents to services. This isn’t an incremental change. It’s a fundamental restructuring of how software gets built, deployed, and consumed. Organizations that understand and implement agentic architecture will own the next decade of software. Those that don’t will watch their monolithic applications become as irrelevant as mainframe terminals.

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The Architectural Evolution

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Where We’ve Been

The web’s evolution tells a story of increasing connection and capability. In the 1990s, we connected static documents through hyperlinks. The social web of the 2000s connected people through platforms. The app web of the 2010s gave us powerful vertical silos, each owning its interface, logic, and data. Each phase delivered value but introduced new constraints. The app web, in particular, created the modern enterprise IT landscape: hundreds of SaaS applications, each brilliant in isolation, collectively forming an integration nightmare.

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Why the App Web Is Breaking

Three systemic failures are making the current architecture unsustainable: Integration debt compounds exponentially. Every enterprise system requires custom integration with every other system it touches. A company with 200 SaaS applications faces potential 19,900 integration points. Most build a fraction of these, accepting workflow friction as the cost of modern IT. Context fragments across boundaries. A single business process might span CRM, ERP, messaging, and analytics systems. Humans become the context routers, copying and pasting between applications, maintaining state in their heads. Knowledge workers spend 40% of their time on this context switching. Vendor lock-in prevents innovation. The deeper you integrate with a platform, the harder it becomes to leave. Your customizations, workflows, and data schemas become chains. Switching vendors means rebuilding from scratch, retraining everyone, and accepting months of reduced productivity.

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The Agentic Architecture

The agentic web replaces app silos with composable, service-centric infrastructure. Instead of humans navigating between applications, AI agents orchestrate services dynamically based on intent. This architecture differs fundamentally from the app web: Service-first design. Systems expose capabilities as composable services, not monolithic applications. Each service does one thing well and describes itself to agents through protocols like MCP. Context-aware routing. User intent and context flow through the system, enabling multi-service workflows without human stitching. The context router maintains state across service boundaries. Governed by design. Policies, permissions, and audit trails are enforced at the protocol layer, not within each application. Governance becomes consistent and comprehensive.

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Context: The Critical Layer

The agentic architecture doesn’t work without proper context engineering. AI agents need more than access to services. They need context: understanding of what tools are available, what data they can access, what policies constrain them, and what state they must maintain. This is where Pylee’s context engineering platform becomes essential. We don’t just host MCP servers. We provide the complete infrastructure for building and governing the context layer:
  • MCP servers become context sources, exposing bounded domains of capability
  • Private registries organize context into manageable, versionable units
  • Policy engines ensure context is used appropriately and safely
  • Audit trails show how context flows through the system
Without context engineering, the agentic architecture is just a clever idea. With it, through platforms like Pylee, it becomes production reality. Learn more about Context Engineering and how it enables the architectural transformation.

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Building Agent-Native Systems

Moving from theory to practice requires understanding how agents interact with services. Agent-native design isn’t about adding AI features to existing applications. It’s about restructuring systems so agents can discover, understand, and orchestrate them autonomously.

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Core Design Patterns

Task Decomposition Agents work best with discrete, well-defined operations. Instead of exposing a single overloaded endpoint like updateOrder, provide focused operations: 
// Agent-native design
{
  "operations": [
    {"name": "createOrder", "params": ["customerId", "items"]},
    {"name": "modifyLineItem", "params": ["orderId", "lineId", "quantity"]},
    {"name": "cancelOrder", "params": ["orderId"], "reversible": true}
  ]
}
Each operation should be atomic, idempotent where possible, and clearly scoped. This allows agents to compose complex workflows from simple building blocks. Semantic Error Handling Agents can’t interpret vague error messages. They need structured, actionable error responses: 
// Instead of "Something went wrong"
{
  "error": {
    "type": "RATE_LIMIT_EXCEEDED",
    "recoverable": true,
    "retry_after_ms": 60000,
    "fallback_action": "use_cached_data",
    "human_message": "System is busy, will retry in 1 minute"
  }
}
This enables agents to recover from failures autonomously, choosing appropriate fallback strategies without human intervention. State Management and Rollback Every state-changing operation needs a compensating action. If an agent can create an order, it must be able to cancel it. If it can allocate resources, it must be able to release them. This enables complex orchestrations with automatic rollback on failure: 
// Every action has a compensating action
{
  "action": "createOrder",
  "compensating_action": "cancelOrder",
  "state_token": "ord_xyz_v1",
  "can_rollback_until": "2024-01-01T00:00:00Z"
}

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Orchestration at Scale

Real-world agentic systems aren’t single agent to single service connections. They’re networks of agents orchestrating dozens of services simultaneously. Service Composition An HR workflow might compose multiple services: query employee database, check calendar availability, send Slack notifications, update payroll system, and log actions to compliance service. Each service exposes discrete capabilities through MCP. The agent understands what each can do and orchestrates them toward the goal. Observability and Tracing When agents make hundreds of service calls, debugging requires comprehensive observability: 
{
  "trace_id": "workflow_abc123",
  "span_id": "service_call_xyz",
  "operation": "updateEmployeeRecord",
  "duration_ms": 145,
  "service": "hr_system",
  "result": "success",
  "tokens_consumed": 1250
}
Every call should be traceable across services, with correlation IDs linking related operations. This enables both debugging and cost allocation. Transport Optimization Different interaction patterns require different transports. Synchronous operations might use HTTP. Streaming responses need Server-Sent Events. Real-time collaboration requires WebSockets. The architecture should support multiple transports, letting each service choose what’s optimal: 
// Service declares supported transports
{
  "service": "analytics_engine",
  "transports": [
    {"type": "http", "latency_ms": 50, "use_for": "queries"},
    {"type": "sse", "use_for": "streaming_results"},
    {"type": "websocket", "use_for": "collaborative_sessions"}
  ]
}
Sections removed from original documents:
  • Detailed history of each web era (condensed to key points)
  • Repetitive examples of business problems (consolidated)
  • Generic “next steps” sections (will add Pylee-specific ones)
  • Redundant explanations of MCP (assumed knowledge from other docs)

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The Transformation Path

Understanding the architecture is step one. Implementation requires a pragmatic, phased approach that delivers value while managing risk.

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Phase 1: Expose Core Services (Months 1-3)

Start with your three most integrated services. These are typically the ones developers already build custom integrations for repeatedly. Wrap them in MCP servers with read-only operations initially. This limits risk while proving the pattern. Deploy these to a private registry. Give access to your innovation team or platform engineering group. Measure adoption, gather feedback, and refine the patterns before broader rollout.

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Phase 2: Establish Governance (Months 3-6)

Before scaling, implement the control plane. This includes service registry for discovery, policy engine for access control, audit logging for compliance, and version management for stability. The governance layer isn’t optional. It’s what separates experimental prototypes from production infrastructure. Platform teams that skip this step end up with ungoverned sprawl that’s worse than the original problem.

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Phase 3: Enable Orchestration (Months 6-9)

Add write operations to your MCP servers. Implement compensating actions for rollback. Deploy your first multi-service workflows. This is where the architecture’s value becomes undeniable. A typical first orchestration might be employee onboarding: create accounts across systems, schedule training, order equipment, notify team members. What took hours of manual work happens in seconds.

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Phase 4: Scale Across the Organization (Months 9+)

With patterns proven and governance established, expand rapidly. Each team wraps their services in MCP. The service registry grows from dozens to hundreds of entries. Agents become more capable as more services become available. This is where network effects accelerate. Each new service makes every agent more powerful. Each new agent workflow validates the investment in services.

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Why This Architecture Wins

The agentic architecture isn’t just technically superior. It fundamentally changes the economics and dynamics of software. Integration becomes free. Once a service implements MCP, any agent can use it immediately. No more point-to-point integrations. No more API documentation. No more client libraries. Innovation accelerates. New workflows are composed, not coded. Business logic moves from compiled code to agent orchestration. Changes that took months now take minutes. Lock-in disappears. Services become interchangeable. If your CRM vendor disappoints, swap in another MCP-compatible service. The agents adjust automatically. Governance scales. Policies apply uniformly across all services. Audit trails are comprehensive by default. Compliance becomes provable, not promised.

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The Competitive Reality

This transformation is happening whether organizations participate or not. Microsoft is rebuilding its entire business application stack around agents. Google is embedding agentic capabilities throughout Workspace. Anthropic, OpenAI, and others are racing to define the standards. Early movers are already establishing position. They’re defining the protocols, owning the registries, and setting the governance patterns. In five years, they’ll be the infrastructure everyone else depends on. The choice isn’t whether to adopt agentic architecture. It’s whether to be part of the infrastructure or dependent on it. Whether to define the standards or follow them. Whether to own the future or rent it.

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Making It Real with Pylee

Pylee provides the production infrastructure for agentic architecture. We handle the complexities of service discovery, authentication, versioning, and governance so you can focus on building services.
Platform teams: Pylee gives you the control plane for agentic architecture from day one. Private registries, RBAC, audit trails, and version management built in.
1

Start with Services

Wrap your first service in MCP using our Hello World Tutorial
2

Establish Governance

Deploy private registries with our Platform Overview
3

Scale Orchestration

Build multi-service workflows with our Deployment Guide

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Next Steps

Platform Overview

Implement agentic architecture in your organization

Build Your First Service

Create an MCP server in 10 minutes

Why Build MCP

The strategic case for MCP investment

Context Engineering

Advanced patterns for agent systems