AI Agent Architecture: How to Make It Efficient and Scalable?

AI is no longer a one-off application or an isolated feature — it has become an essential part of corporate infrastructure. Today, the traditional software development model faces a limiting barrier: deterministic systems, designed strictly for human interactions, cannot accommodate the autonomy and dynamism of components that plan and act independently, such as AI agents.

This new scenario requires architects and software engineers to rethink corporate infrastructure through the lens of a new system design discipline: agentic architecture.

‍What is agentic architecture?

Agentic architecture is the design of AI systems capable of planning, using tools, accessing contexts, collaborating, and executing actions autonomously through the operation of AI agents. Unlike traditional software architecture, it focuses on building robust, non-deterministic systems that can operate under strict control, observability, security, and corporate governance.

‍What defines the anatomy of an AI agent?

Although the market broadly discusses the use of agents, their technical construction follows a well-defined standard anatomy. An AI agent is structured around the following components:

- Foundation Model: the central probabilistic engine that processes inputs and outputs. It is not necessarily limited to an LLM (Large Language Model); it can consist of a smaller model (SLM) or foundational audio, video, and image models.

- Instructions (Meta Prompts/System Prompts): policies, criteria, and guidelines that define the model's execution rules and operational boundaries.

- Retrieval (Retrieval/Query Results): the ability to dynamically search external data to feed the agent's reasoning.

- Tools (Tool/Call Response): APIs and integrations exposed so the agent can take real actions in the external world.

- Memory (Memory/Read/Write): mechanisms for writing and reading information, divided into short-term and long-term.

‍‍Related content: How to prepare your APIs for AI agents?

‍What is essential when building multi-agent systems?

To build efficient, reliable, and scalable multi-agent systems, engineers must not focus solely on the intelligence algorithm itself. It is essential to design four fundamental concepts in a cross-cutting manner within architectural definitions:

- Context: ensuring the AI has access to the right data at the exact right moment.

- Control: establishing security and cost boundaries and restrictions for the AI agent's behavior.

- Integration: standardizing connections and the vertical and horizontal communication of agents with the ecosystem.

- Operations: monitoring, auditing, and optimizing the system's technical and financial execution cycle.

‍What is Context Engineering?

Traditional "prompt engineering" lost ground as systems evolved toward the agentic model. Today, the isolated prompt matters less than the ability to dynamically manage the flow of information that feeds the AI agent. Context Engineering is the architectural practice of managing, optimizing, and selecting the information present in a model's context window to ensure decision accuracy, contain hallucinations, and optimize processing costs.

‍What are the pillars of Context Engineering?

Context Engineering rests on four fundamental data flow design pillars:

- Write: records interactions in real time (at the session or thread level). It uses tools such as scratchpads to summarize and consolidate history so that unnecessary data does not saturate the context window.

- Select: retrieves relevant data from corporate sources. The best market practice is to use Advanced RAG (Retrieval-Augmented Generation) architectures, implementing hybrid searches (vector data combined with relational keyword searches) combined with re-ranking algorithms to select the "top 3" or "top 5" most correlated results.

- Compress: synthesizes long-term interaction history. Instead of storing and resending extensive raw chat histories, the architecture compresses the content into structured summaries linked to the session ID, drastically reducing token consumption.

- Isolate: prevents context poisoning, ensuring that outdated, conflicting, or malicious data does not interfere with the foundation model's logical reasoning, thus avoiding hallucinations.

‍What is long-term memory?

Unlike short-term memory (limited to the active session), long-term memory persists historical data that gives consistency to the AI agent's behavior over time. It is divided into three types:

- Semantic Memory: stores consolidated facts and information about the user or the agent's own domain.

- Episodic Memory: keeps the history of the AI agent's past experiences and actions (whether a previous task was executed successfully or generated failures), helping it learn from its execution history.

- Procedural Memory: consolidates rules of conduct, operational instructions, and system prompts, detailing what the agent should and should not do throughout its tasks.

Related content: 5 challenges when scaling strategies based on AI agents

What is Harness Engineering?

The concept of Harness Engineering refers to building a robust support infrastructure around the AI agent. This infrastructure acts as a protective armor that guides and monitors model execution through two essential components:

- Guides: act before the agent's action (feedforward). They consist of playbooks, contract specifications and tool schemas, cost/autonomy restrictions, and security playbooks to reduce errors before the action happens.

- Sensors: act after the agent's reasoning and execution (feedback). They include distributed execution traces, agent trajectory logs, deterministic type checks, groundedness validations (whether the response is based on the retrieved facts), LLM-as-a-judge techniques, and human-in-the-loop approvals to reduce harm after the action.

‍What are Evaluations?

Evaluations (or Evals) consist of creating automated tests and continuous feedback loops specifically designed to assess AI models. An evaluation loop operates under a continuous cycle of specifying criteria, measuring performance, and improving instructions. Agentic architectures use an Evaluation Suite (test suite) to run repetitive task scenarios, validating deterministic aspects (linters), tool calls, trajectory compliance, token consumption, and latency.

‍What are the best practices for implementing an agentic architecture?

Keeping in mind that in software architecture there is no single solution that will solve absolutely all your problems and that everything is a matter of trade-offs, systems can be structured across three isolated contexts with cross-cutting governance:

‍1. Human Context (Experience Layer)

- Gradualism in autonomy: do not grant AI agents full (100%) autonomy on day one. Progressively implement human-in-the-loop (HITL) or human-on-the-loop (HOTL) to oversee critical actions.

- Specification-Driven Development (SDD/SPDD): use approaches such as Spec Driven Development (where the model reads structured technical specifications to act) and Structured Prompt Development Driven to ensure a robust, standardized semantic interaction design.

‍2. Agentic Context (Reasoning Layer)

- Decoupling and identity security: create dedicated identities for agents (Agent Identity). Implement secure OAuth-based authorization token delegation mechanisms (such as the On-Behalf-Of (OBO) pattern), ensuring the agent executes operations only within the logged-in user's permissions.

- Orchestration modularity: centralize task planning, orchestration workflows, and horizontal (A2A) communication in neutral buses that manage the context window efficiently.

‍3. Systemic Context (Integration Layer)

- Redesign of corporate APIs: AI agents interact differently from humans or traditional integrations. Domain APIs and the MCP servers that expose corporate data need to be optimized, performant, and semantic to support this new, massive automated traffic.

Related content: What is MCP and how to use it in your AI strategy?

What is AIOps?

AIOps (applied to AI infrastructure management) is the discipline focused on monitoring and ensuring the efficiency and technical and operational performance of AI-based systems. It leverages proven patterns from traditional software engineering, extending them to the probabilistic particularities of language models.

‍In AIOps, what ensures operational and technical efficiency and performance?

Technical excellence in AI runtime depends on rigorously tracking indicators such as:

- Traces, logs, and trajectories: monitoring the exact reasoning path and tool calls executed by the AI agent.

- TTFT (Time to First Token) and latency: a critical metric that measures the time elapsed from the prompt to the generation of the first character of the response.

- Groundedness, hallucination, and correctness: verifying whether the generated responses are truthful relative to the data provided in the context window.

- Tool success rate and fallback rate: the success percentage of the APIs called by the AI agent and the frequency with which the system needs to trigger alternative routes.

- SLOs and alerts: defining acceptable operational service-level boundaries.

‍In AI FinOps, what ensures financial efficiency and business value?

Since processing high-capacity AI models is not cheap, financial engineering (FinOps) must manage return on investment through:

- Token tracking: controlling the volume of tokens consumed on input (input context) and output (generation).

- Token budgets, quotas, and anomaly detection: blocking costs generated by AI agents that enter infinite retry loops.

- Cost per workflow and tool: identifying which processes deliver real value relative to computational consumption.

- Structured optimizations: using techniques such as Prompt Caching (cost reduction for repetitive prompts), model routing by complexity (directing simple tasks to cheaper models), and context window compression.

‍Conclusion

The race for enterprise AI has shifted focus. An organization's real competitive advantage no longer lies in simply consuming off-the-shelf public models, but rather in its technical capacity to architect data, context, memory, and connections engineering in a secure, governed, and scalable way.

By structuring an agentic architecture based on Context Engineering, Harnessing, neutral protocols such as MCP, and operational governance via AIOps and AI FinOps, large enterprises stop treating AI as an isolated experiment and transform it into a fully integrated, highly productive digital workforce.

Want to prepare your company's architecture for AI agents? Talk to our experts now and learn how to start this journey!

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