Beyond the Model: Designing Reliable Agentic AI

Author: Denis Avetisyan

The true key to building trustworthy autonomous agents lies not in model scale, but in the underlying architecture that governs their behavior.

This review argues that architectural design-including component structure, interface definitions, and control loop implementation-is paramount to the reliability of agentic AI systems.

Despite advances in generative AI, building reliably autonomous systems remains a significant challenge. This is the central argument of ‘Architectures for Building Agentic AI’, which posits that architectural design-specifically, the structuring of components, definition of interfaces, and implementation of control loops-is paramount to achieving trustworthy agentic systems. The paper demonstrates how principled componentisation and disciplined interfaces, rather than model capabilities alone, dictate the reliability and failure modes of tool-using agents operating in closed loops. Can a robust architectural framework unlock the full potential of agentic AI and pave the way for truly dependable autonomous decision-making?

The Illusion of Intelligence: Why AI Still Needs a Reset

Conventional artificial intelligence often falters when confronted with tasks demanding sustained reasoning and flexibility in unpredictable environments. These systems, typically designed for narrow, predefined problems, struggle with the open-endedness of real-world scenarios requiring continuous planning and adaptation. Their reliance on static datasets and pre-programmed responses limits their ability to generalize to novel situations or recover from unexpected changes. This inherent rigidity contrasts sharply with human cognition, which excels at long-term goal pursuit, improvisation, and learning from experience-abilities that are increasingly recognized as crucial for advancing the field of artificial intelligence and enabling truly autonomous systems.

The emergence of agentic AI signifies a fundamental departure from conventional artificial intelligence approaches. While traditional AI excels at narrowly defined tasks, often requiring explicit programming for every contingency, agentic systems demonstrate the capacity for autonomous operation, exhibiting proactive decision-making and the ability to leverage tools to achieve complex goals. This isn’t simply about automation; it’s about creating systems that can independently assess situations, formulate plans, and execute those plans through interaction with various digital resources – effectively functioning as independent problem-solvers. This capability opens possibilities for applications requiring adaptability, long-term strategy, and resilience in dynamic environments, moving beyond reactive responses toward truly intelligent action and reshaping how humans interact with technology.

The development of truly reliable agentic AI necessitates a move beyond monolithic designs toward modular architectures. This research details a comprehensive framework built on the principle of componentization – breaking down complex tasks into manageable, reusable modules. Crucially, these components are linked via disciplined interfaces, enforcing clear contracts and reducing emergent behavior. The framework further prioritizes explicit control loops, allowing for continuous monitoring, evaluation, and refinement of the agent’s actions – essentially building in ‘brakes’ and feedback mechanisms. By focusing on these three pillars, the proposed architecture aims to mitigate the risks associated with autonomous systems, fostering predictability and trust while unlocking the potential of AI agents to tackle increasingly complex challenges and operate safely in dynamic environments.

Orchestration is Everything: Planning and Tool Use in the Real World

The Planner component is a foundational element in agentic systems, functioning as the central task decomposition module. Its primary responsibility is to accept a high-level objective and break it down into a sequence of discrete, executable steps. This decomposition process is crucial because most complex goals cannot be directly achieved; instead, they require a series of intermediate actions. The Planner doesn’t necessarily execute these steps – that function is typically handled by an Executor – but rather defines the necessary action sequence. The granularity of these steps can vary significantly depending on the complexity of the task and the capabilities of the agent, ranging from simple API calls to more complex sub-plans. Effective planning requires the Planner to consider available tools, potential constraints, and the desired order of operations to ensure successful task completion.

Tree of Thoughts (ToT) and Graph of Thoughts (GoT) represent advancements beyond traditional sequential planning by enabling agents to explore multiple reasoning paths. ToT frames problem-solving as a tree, where each node represents a thought or intermediate reasoning step, and the agent evaluates and prunes branches based on predefined criteria. GoT extends this by representing reasoning as a graph, allowing for more complex relationships between thoughts and enabling the agent to revisit and refine previous steps without strict linear constraints. Both techniques facilitate more robust exploration of the solution space and improved reasoning capabilities, particularly in complex tasks requiring nuanced evaluation and backtracking, as compared to methods that rely on a single, predetermined plan.

The Tool Router functions as the critical intermediary between high-level planning and action execution within an agentic system. It receives abstract goals formulated by the Planner and translates these into specific tool calls, identifying the appropriate capabilities required to achieve each sub-task. This translation process involves mapping the desired outcome to the available tools and their corresponding parameters. Subsequently, the Tool Router directs these tool calls to the Executor, which then performs the actions in the external environment. Successful operation of the Tool Router is therefore essential for bridging the gap between intention and implementation, enabling the agent to leverage its available tools effectively and progress towards its overall objective.

Safety Nets and Sanity Checks: Monitoring for Inevitable Failure

Agentic systems, due to their autonomous operation within unpredictable environments, necessitate robust safety mechanisms. The Safety Monitor component is specifically designed to fulfill this requirement by continuously assessing planned actions and observed states against predefined safety constraints. This assessment involves verifying that system behaviors remain within acceptable boundaries and do not pose risks to the environment or to the system itself. The Safety Monitor operates independently of the core agent logic, providing a critical layer of oversight and intervention capability. It can halt potentially unsafe actions, trigger recovery procedures, or escalate issues to human operators, thereby mitigating the risks associated with autonomous decision-making in complex scenarios.

The Schema Enforcement and Verifier components are critical for ensuring agentic systems operate within defined boundaries and achieve intended outcomes. Schema Enforcement validates proposed plans against a predefined schema, confirming that actions adhere to permissible parameters and data types before execution. Following action completion, the Verifier component assesses the results against expected outputs, utilizing defined criteria to detect discrepancies or unintended consequences. This dual-validation process – pre-execution schema checks and post-execution result verification – minimizes the risk of erroneous or harmful actions by identifying and flagging violations before they impact the environment or system state. Both components operate independently and can trigger corrective measures, such as plan modification or execution halting, based on the severity of the detected issue.

Telemetry data within agentic systems encompasses a range of performance metrics, environmental sensor readings, and internal state information continuously streamed from the agent during operation. This data is processed in real-time to establish baseline behaviors and identify deviations indicative of potential failures, resource constraints, or unexpected interactions with the environment. Proactive issue detection is achieved through configurable thresholds and anomaly detection algorithms applied to the telemetry stream, triggering automated alerts or corrective actions. Resolution capabilities include dynamic resource allocation, plan adjustments, or safe shutdown procedures, all initiated based on telemetry-derived insights, thereby minimizing downtime and ensuring operational stability.

A Patchwork of Possibilities: Diverse Architectures and Limited Applications

Agentic systems aren’t built on a single blueprint; instead, their architecture varies considerably based on the demands of the task at hand. Reactive agents, the simplest form, operate on a stimulus-response basis, ideal for immediate, predictable environments. More complex deliberative agents employ planning, reasoning, and knowledge representation to tackle intricate problems, though they can be slower to respond. Increasingly, hybrid architectures combine the strengths of both, allowing for rapid reactions in routine situations while reserving deeper cognitive processes for novel challenges. This adaptability is crucial; a robotic vacuum cleaner might utilize a reactive approach for obstacle avoidance, but a financial trading bot requires the deliberative capacity to analyze market trends and execute complex strategies. Consequently, the choice of architecture fundamentally shapes an agent’s capabilities and its suitability for a given application.

The versatility of agentic systems extends across a remarkable spectrum of applications, ranging from the digital to the distinctly physical. Web Agents autonomously navigate the complexities of online services, automating tasks like information gathering, e-commerce transactions, and content aggregation. Conversely, Embodied Agents venture beyond the screen, existing as robotic entities or software controlling physical devices, enabling applications in areas like logistics, manufacturing, and even personal assistance. This duality highlights a key strength: the same underlying principles of autonomy and interaction can be adapted to address challenges in both virtual and real-world domains, promising increasingly seamless integration between the digital and physical realms.

The burgeoning field of multi-agent systems is experiencing rapid advancement, largely due to the emergence of specialized development frameworks. Tools like AutoGen and LangGraph significantly lower the barrier to entry for researchers and developers, offering pre-built components and simplified interfaces for constructing complex agent interactions. These frameworks automate much of the boilerplate code traditionally required for agent communication, task orchestration, and workflow management, enabling faster prototyping and experimentation. By abstracting away the intricacies of agent coordination, AutoGen and LangGraph empower creators to focus on defining agent roles, knowledge bases, and overall system goals, ultimately accelerating innovation across diverse applications – from automated customer service and content creation to scientific discovery and complex problem-solving.

The Illusion of Learning: Memory, Collaboration, and the Limits of AI

The capacity for artificial agents to learn and adapt is being fundamentally reshaped by innovations in memory integration. Rather than processing each new situation in isolation, contemporary systems are increasingly employing techniques like Memory-Augmented Agents and dedicated Memory Layers. These approaches allow agents to store and retrieve past experiences – effectively building a personal history – which then informs future decision-making. This isn’t simply about recalling facts; the agent learns to recognize patterns, anticipate consequences, and refine its strategies based on what has previously succeeded or failed. Consequently, performance isn’t just improved through algorithmic optimization, but through a form of accumulated experiential knowledge, allowing these agents to tackle complex, dynamic challenges with greater robustness and efficiency. The incorporation of memory transforms agents from reactive systems into proactive learners, paving the way for more intelligent and versatile artificial intelligence.

The potential for complex problem-solving dramatically expands when individual agents collaborate within multi-agent systems. Rather than relying on a single, monolithic entity, these systems distribute tasks and leverage the unique strengths of each agent – one might excel at data gathering, another at strategic planning, and yet another at execution. This division of labor, combined with effective communication and coordination, allows the system to tackle challenges that would be insurmountable for any single agent. Such collaborative architectures are proving particularly effective in dynamic and unpredictable environments, where adaptability and resilience are paramount, and are being explored in fields ranging from robotic swarms and traffic management to financial modeling and disaster response. The efficiency gains aren’t merely additive; the interaction between agents often generates emergent behaviors and innovative solutions unavailable through isolated action.

The pursuit of truly intelligent agents hinges on moving beyond reactive behaviors to systems capable of deliberate reasoning, and the Belief-Desire-Intention (BDI) architecture offers a robust framework for achieving this. This computational model posits that an agent’s actions are driven by its beliefs about the world, its desires for particular outcomes, and its intentions – the specific plans it adopts to fulfill those desires. By explicitly representing these cognitive states, BDI agents can not only consider the likely consequences of actions but also evaluate them in relation to long-term goals, allowing for more flexible and adaptive problem-solving. Instead of simply responding to stimuli, a BDI agent can reason about what it wants to achieve, whether a given action will help, and how to execute a plan even in the face of uncertainty or changing circumstances. This principled approach to agent design is proving crucial in developing systems capable of complex tasks, from autonomous robotics to sophisticated software assistants, and represents a significant step towards artificial general intelligence.

The pursuit of agentic AI architectures feels less like innovation and more like meticulously crafting elaborate failure modes. This paper correctly identifies that robust design-tightly coupled components, well-defined interfaces, and predictable control loops-offers the only path toward something resembling reliability. It’s a bracing acknowledgement that model capability is secondary to how those capabilities are contained. As Robert Tarjan observed, “The most effective algorithms are often the simplest.” This sentiment echoes through the discussion of control loops; complex emergent behavior isn’t a feature, it’s a bug. The bug tracker, inevitably, will fill with tales of agents ‘optimizing’ toward unintended consequences. They don’t deploy-they let go.

What’s Next?

The insistence on architecture as the linchpin of agentic AI reliability feels, predictably, like moving the problem. It’s not that robust design is unimportant – merely that any design, no matter how elegantly conceived, will eventually encounter an input it was not designed to handle. The field will rapidly discover that ‘agentic’ systems, left to operate at scale, exhibit failure modes previously confined to theoretical discussions of distributed systems. Better one meticulously tested control loop than a hundred loosely coupled, self-optimizing black boxes, a lesson repeatedly learned and apparently perpetually forgotten.

Verification, naturally, remains the core challenge. The current enthusiasm for tool-using agents conveniently skirts the issue of validating those tools. A system is only as trustworthy as its least audited component, and the assumption that large language models can self-certify their actions will likely prove… optimistic. Expect a surge in research dedicated to formal methods, runtime monitoring, and the increasingly desperate attempt to build guardrails around systems fundamentally designed to circumvent them.

The pursuit of ‘general’ agency will undoubtedly lead to architectures of escalating complexity. The history of software development suggests this complexity will not be gracefully managed. It’s a safe prediction that the most interesting research will not be in building more powerful agents, but in understanding – and mitigating – the inevitable cascade failures that arise when those agents collide with the messy reality of production environments.

Original article: https://arxiv.org/pdf/2512.09458.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/