Building an Agentic Application: A Blueprint for Scalable Intelligence

Imagine this: your team is tasked with building an enterprise-grade GenAI agent—one that doesn't just respond to prompts, but perceives, reasons, acts, learns, and collaborates. Not a chatbot, but an agentic system that interacts with APIs, fetches insights, makes decisions, and gets better over time.

Where do you start? What do you need? How do you build this to scale on-premise, securely, and modularly?

Let's walk through the real story of building an agentic application in 2025—layer by layer, like an engineer, an architect, and a futurist all in one room.

Executive Summary

The Business Case for Agentic AI

Agentic applications represent the next evolution of enterprise AI—moving beyond simple query-response systems to autonomous, goal-oriented intelligence that can perceive, reason, act, and collaborate. Unlike traditional AI implementations that require constant human oversight, agentic systems can independently execute complex workflows, make contextual decisions, and coordinate with other agents to accomplish multi-step objectives.

Key Value Propositions:

• Autonomous Operations: Agents handle routine tasks end-to-end, freeing human expertise for strategic work

• Intelligent Collaboration: Multi-agent systems can tackle complex problems that no single AI model could solve alone

• Adaptive Learning: Systems improve through feedback loops and continuous learning from outcomes

• Enterprise Integration: Seamless connection with existing business systems, APIs, and workflows

A Note on Technology Choices: The tools mentioned here represent categories of solutions, not prescriptions. While agentic AI is an evolving market, the core building blocks—from orchestration platforms to LLM serving to agent frameworks—are mature enough for enterprise deployment. The goal is to show that you have viable options across every layer needed to build production agentic systems today

Architecture Note: While this blueprint presents 7 distinct layers for comprehensive understanding, these could be consolidated into fewer layers. However, I've deliberately separated each architectural component to provide granular insight into every critical aspect of agentic workflows. The detailed breakdown helps teams understand dependencies and implement components incrementally based on their specific needs.

Phase 1: Foundation & Core Services

Level 1: Foundation – Infrastructure & Platform Services

Every intelligent system needs a strong skeleton. This is where your agentic application starts—in the compute layer that powers everything above it.

🔹 Compute & OS – Dell AI Servers provide optimized hardware foundations, typically running Ubuntu or CentOS for stability with multiple GPU options from Nvidia, AMD and Intel. But here's where it gets interesting: GPU-powered nodes with NVIDIA MIG (Multi-Instance GPU) slice compute efficiently, allowing multiple agents to share GPU resources without interference. This means your perception agent can process vision data while your reasoning agent handles language models simultaneously.

🔹 GPU Orchestration – Tools like RunAI or Kubernetes + NVIDIA Operator dynamically allocate GPU tasks across agents. Think of it as a smart traffic controller that ensures your most critical agents get the compute they need when they need it, automatically scaling up for heavy reasoning tasks and scaling down during idle periods.

🔹 Containerization – Every agent runs in its own Docker/Podman container—lightweight and isolated. This isn't just about deployment; it's about agent autonomy. Each agent can have its own dependencies, Python versions, and configurations without stepping on each other's toes.

🔹 Orchestration – Kubernetes becomes the backbone for deployment and scaling. It manages agent lifecycle, handles failovers, and ensures that if one agent crashes, others continue running. Your agent ecosystem becomes resilient by design.

🔹 Storage – Dell storage solutions optimized for unstructured data, along with Ceph or MinIO, store models, agent memory, and logs reliably. This distributed storage ensures that agent memories persist across restarts and can be shared between agents when needed for collaborative tasks.

🔹 Networking – Cilium and Istio power secure, observable agent-to-agent communication. Every message between agents is encrypted, monitored, and can be traced for debugging complex multi-agent workflows.

🔹 Secrets Management – Vault ensures API keys and model credentials are encrypted and scoped properly. Each agent gets only the credentials it needs, following the principle of least privilege.

Why it matters: Without this foundation, nothing runs. Agents need fast compute, isolated environments, reliable storage, and secure networking. This layer is invisible to end users—but absolutely critical for enterprise-grade deployment.

Level 2: Core Services – Data, Security, and Communication

Now your system needs to talk, share, and remember. This is where agents transform from isolated processes into a collaborative network.

🔹 Messaging (Kafka/NATS) – Agents don't call each other directly. They publish observations, intentions, or events using publish-subscribe messaging. A planning agent publishes "task decomposed" events, execution agents subscribe to relevant tasks, and validator agents listen for completion events. This creates loose coupling—agents can be added, removed, or updated without breaking the entire system.

🔹 Agent-to-Agent (A2A) Protocols – These define the language and intent semantics agents use when communicating. Unlike basic messaging, A2A protocols structure how agents discover each other's capabilities, negotiate task delegation, and share context. An agent publishes an "Agent Card" describing its skills, other agents can then request specific capabilities, and the system orchestrates the handoff.

🔹 API Gateway (Kong, TYK) – Secure API exposure with rate limiting for internal/external tools. Your agents can safely interact with external APIs while maintaining security boundaries and preventing any single agent from overwhelming external services.

🔹 Data Pipelines (NiFi/Airbyte) – Data flows in from logs, documents, sensors—processed before agents touch it. This ensures agents receive clean, structured data and can focus on reasoning rather than data cleaning.

🔹 Vector Databases (Qdrant/pgvector/Milvus) – Semantic storage for context, memories, and knowledge. Agents can store and retrieve information based on meaning rather than exact matches, enabling more intelligent context awareness.

🔹 Caching (Redis) – Fast lookups for agent profiles, policies, and recent answers. Reduces latency for frequently accessed information and enables quick agent state recovery.

🔹 Identity & Access Management (OPA + SPIFFE) – Agents and users authenticated and authorized at every step. Each agent has a cryptographic identity, and policies define what each agent can do, where it can go, and what data it can access.

🔹 Observability (Prometheus, Grafana) – Visualize metrics, set alerts, and monitor agent health. Track agent performance, identify bottlenecks, and get early warning when agents start behaving unexpectedly.

Why it matters: This is your connective tissue. Without it, agents are isolated bots. With it, they become part of a living, breathing system that can adapt, scale, and collaborate intelligently.

Phase 2: Intelligence & Coordination

Level 3: AI & Cognitive Services – Making the System Smart

This is where the magic begins. Your agents start to reason, recall, and perceive the world around them.

🔹 LLM Serving (vLLM, TGI) – Deploy large language models with low latency. vLLM optimizes memory usage and throughput, while TGI (Text Generation Inference) provides efficient batching. Your reasoning agents can now process multiple queries simultaneously without performance degradation.

🔹 Embeddings & RAG (LangChain + BGE) – Agents retrieve before they generate, grounded in your data. BGE (BAAI General Embedding) creates high-quality embeddings, while LangChain orchestrates the retrieval process. This means agents can access company knowledge, previous conversations, and domain-specific information to provide accurate, contextual responses.

🔹 Model Registry (MLflow, HuggingFace Hub) – Track versions and rollbacks of models and checkpoints. Dell Enterprise Hub by Hugging Face provides hardware-optimized model repositories specifically tuned for Dell infrastructure. As your agents learn and improve, you can safely deploy new model versions, compare performance, and roll back if needed.

🔹 Learning (DSPy, RLHF) – Agents adapt with feedback loops. DSPy helps agents learn to compose better prompts, while RLHF (Reinforcement Learning from Human Feedback) enables agents to improve based on user preferences and outcomes.

🔹 Memory Systems – Redis for Short-Term Memory (STM) and Qdrant for Long-Term Memory (LTM). Agents remember recent conversations and can recall relevant information from months ago. This creates continuity and personalization in agent interactions.

🔹 Model Context Protocol (MCP) – Models are no longer stateless APIs. With MCP, agents can route prompts with context-awareness, manage token windows intelligently, and extract latent capabilities. This is crucial in multi-agent scenarios where context must be shared and maintained across different specialized agents.

🔹 Multimodal AI (WhisperX, OpenVINO) – Beyond text, agents interpret voice, images, and video. WhisperX provides accurate speech transcription, while OpenVINO optimizes computer vision models for edge deployment.

Why it matters: This layer gives your agents brains and senses. But a brain without a body still doesn't accomplish much...

Level 4: Agent Layer – Goal-Oriented Autonomy

Now we build the agents themselves—each with a distinct role, specialized capabilities, and autonomous decision-making.

🔹 Planner Agents (CrewAI, AutoGen 2.0) – They receive high-level goals and break them down into actionable subtasks. CrewAI excels at role-based orchestration, assigning tasks to specialist agents, while AutoGen 2.0 enables conversational planning where agents negotiate and refine plans through dialogue.

🔹 Executor Agents (LangChain + FastAPI) – Handle API calls, database queries, and user instructions. These agents are the "hands" of your system, actually performing actions in the real world. They can update CRM records, send emails, trigger workflows, and interact with external systems.

🔹 Validator Agents (DSPy, Rebuff) – Embedded reviewers for quality, bias, and safety. Unlike global guardrails, these are localized AI validators that dynamically assess actions or answers within workflows. They can catch errors, ensure compliance, and maintain quality standards specific to each task domain.

🔹 Analyzer/Synthesizer Agents (PandasAI, Orq.ai) – Process reports, visualize trends, and summarize data. These agents transform raw data into insights, create visualizations, and generate executive summaries that humans can act upon.

🔹 Collaborator Agents (CrewAI) – Teams of agents solve complex workflows together. Multiple agents can work in parallel, share context, and coordinate their outputs to accomplish tasks that no single agent could handle alone.

🔹 Voice Agents (LiveKit) – Interfaces that can listen and respond in voice or vision. These agents provide natural language interfaces for human interaction, supporting real-time conversation and multimodal input.

Why it matters: These are your autonomous workers—like a digital workforce where each agent is tuned for specific tasks but can collaborate seamlessly. They transform your infrastructure into an intelligent, goal-oriented system.

Phase 3: Orchestration & Human Integration

Level 5: Orchestration & Workflow Management

Even autonomous agents need coordination. This layer provides the "management" that ensures agents work together effectively.

🔹 Visual Orchestration (n8n, Node-RED) – Non-technical users can drag & drop workflows across agents. Business users can create complex agent workflows without coding, enabling rapid experimentation and iteration.

🔹 Durable Workflow Engines (Temporal, Airflow) – Mission-critical flows that persist through failure or restart. Temporal ensures workflows complete even if individual agents fail, while Airflow manages complex dependencies between agent tasks.

🔹 Multi-Agent Coordination (AutoGen 2.0) – Choreograph conversations, planning rounds, and task delegation. Agents can engage in structured dialogues, debate solutions, and reach consensus on complex decisions.

🔹 Triggers (CRON, Rundeck) – Agents act on time, events, or system conditions. Your system becomes proactive, responding to schedule changes, system alerts, and business events automatically.

Why it matters: This layer gives you control and reliability. It's the difference between agents doing one-off tasks versus operating continuously and reliably as an integrated system.

Level 6: Human Interaction & Interface

Now it's time to build bridges between your intelligent system and the humans who use it.

🔹 Web/API Interfaces (FastAPI, gRPC) – Developers and external tools communicate with the system. FastAPI provides intuitive REST APIs, while gRPC enables efficient, type-safe communication for high-performance applications.

🔹 GUI Dashboards (Streamlit, Retool) – Operations teams view insights, control agents, and monitor workflows. Streamlit enables rapid prototyping of analytical interfaces, while Retool provides professional dashboard building for operations teams.

🔹 CLI & Automation Tools (Typer) – Administrators manage via scripts and terminals. Command-line interfaces enable automation, scripting, and integration with existing DevOps workflows.

🔹 Voice/Multimodal Interfaces (Riva) – End users speak to agents or upload documents/images. NVIDIA Riva provides enterprise-grade speech recognition and synthesis, enabling natural conversation with your agent system.

🔹 Enterprise Integration (Slack bots, Teams) – Agents send updates, summaries, and alerts to human coworkers. Your agents become team members, participating in conversations and providing timely updates through familiar communication channels.

Why it matters: This is where your agent stops being a black box and starts becoming a valuable team member. Humans can understand, direct, and collaborate with your intelligent system.

Phase 4: Governance & Optimization

Level 7: Governance, Guardrails, and Feedback Loops

At enterprise scale, intelligence needs trust, control, and continuous improvement.

🔹 Guardrails (Guardrails.ai, JSONSchema) – Constrain what agents can say or do. Guardrails.ai provides semantic validation, while JSONSchema ensures structured output formats. These prevent agents from generating harmful content or taking unauthorized actions.

🔹 Human-in-the-Loop (Slack approvals) – For sensitive actions, humans intervene. Critical decisions, large financial transactions, or policy changes require human approval before execution.

🔹 Audit & Compliance (MinIO logs, dashboards) – Traceability across actions for trust and regulation. Every agent action is logged, timestamped, and can be traced back to the original trigger and decision logic.

🔹 Security (Falco, Zenity) – Runtime monitoring for threats or anomalies. Falco detects unusual behavior in agent containers, while Zenity monitors agent communications for potential security issues.

🔹 Testing (pytest-agents, LangSmith) – Agents undergo QA like any other software. LangSmith provides observability for agent interactions, while specialized testing frameworks ensure agent behavior remains consistent and reliable.

🔹 Continuous Feedback (LangChain Feedback, RLHF) – Agents learn from outcomes, not just inputs. User feedback, task outcomes, and performance metrics feed back into agent training, enabling continuous improvement.

Why BUILT-IT Architecture matters

✅ Layered Foundation: Each layer builds on the last, adding capability, trust, and abstraction. You can't have intelligent agents without compute, communication without security, or governance without observability.

✅ Horizontal Resilience: Components work in parallel, creating a resilient, decoupled system. If one agent fails, others continue. If one service goes down, alternatives can take over.

✅ Open-Source Modularity: Every layer uses open-source tools, giving you the flexibility to scale what you need, when you need it, without vendor lock-in.

✅ Enterprise-Ready: Built for security, compliance, and scale from the ground up. This isn't a prototype—it's production-ready architecture.

The Communication Revolution

Agent-to-Agent (A2A) and Model Context Protocol (MCP) are becoming the grammar of agentic systems. Kafka and gRPC give agents a voice—but A2A defines the message's meaning, enabling capability discovery and task delegation. MCP brings structured cognition when dealing with powerful models, ensuring context flows intelligently between agents and models.

Together, they enable agent teams to think and act coherently, transforming isolated AI tools into collaborative intelligence networks.

A Path to Agentic AI

Building agentic applications isn't just about stringing together AI APIs. It's about creating intelligent systems that can perceive, reason, collaborate, and improve—at enterprise scale, with enterprise security, and reliability.

The architecture outlined here represents current best practices in a rapidly evolving field. Organizations are building these systems today, with tools increasingly reaching production maturity. Dell AI Factory demonstrates how integrated platforms can accelerate development, though the landscape continues to evolve.

The agentic AI ecosystem is showing significant progress. While frameworks and best practices continue to mature, the infrastructure foundation is becoming solid and core patterns are emerging. Organizations should approach deployment thoughtfully as expertise grows across the industry.

The question isn't whether agentic AI will transform how we work—it's how quickly organizations can adapt to this evolving landscape. The market is maturing, tools are becoming more production-ready, and success requires both strategic vision and careful execution.

 What's your next step? Start with the foundation. Build your infrastructure layer, add communication protocols, and begin with simple agents that can already add value. The future of work is collaborative intelligence—and it starts with the right architecture.

#Iwork4Dell #AgenticAI #EnterpriseAI #AIAgents #MachineLearning #AIArchitecture #DellAIFactory #DigitalTransformation #FutureOfWork #AIInfrastructure #TechLeadership #Innovation2025