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\acmDOI{} \acmDOI{}
\acmISBN{} \acmISBN{}
\acmConference[JC3506]{Software Design and Implementation}{2025--2026}{University of Aberdeen} \acmConference[JC3506]{Software Design and Implementation}{2025--2026}{University of Aberdeen}
\settopmatter{printacmref=false}
\renewcommand\footnotetextcopyrightpermission[1]{}
\usepackage{booktabs} \usepackage{booktabs}
\usepackage{float} \usepackage{float}
@@ -33,7 +35,7 @@ Agentic AI---where large language models are embedded in autonomous loops capabl
AI tools have been part of the software engineer's toolkit for years---code completion, static analysis, defect prediction---but they have always operated in a supporting role. The developer decides; the tool assists. What has changed recently is the emergence of systems where that division no longer holds so cleanly. Under the label of \emph{agentic AI}, large language models (LLMs) are now embedded in execution loops that let them perceive their environment, make plans, call external tools, and update their behaviour in response to feedback, all without a human directing each step \cite{abuali2025agentic, wang2024survey}. AI tools have been part of the software engineer's toolkit for years---code completion, static analysis, defect prediction---but they have always operated in a supporting role. The developer decides; the tool assists. What has changed recently is the emergence of systems where that division no longer holds so cleanly. Under the label of \emph{agentic AI}, large language models (LLMs) are now embedded in execution loops that let them perceive their environment, make plans, call external tools, and update their behaviour in response to feedback, all without a human directing each step \cite{abuali2025agentic, wang2024survey}.
For software system design, this shift is not merely incremental---it represents a structural reorientation of the human--machine relationship that classical software architectures did not anticipate. Those architectures draw a sharp boundary between human intent and machine execution: the engineer specifies; the tool executes within tightly scoped preconditions. Agentic systems dissolve that boundary: a single agent, or a coordinated ensemble, can in principle traverse the entire software development lifecycle autonomously---eliciting and formalising requirements, synthesising and compiling code, executing regression suites, and performing static analysis---cycling through these phases in a planning-execution-reflection loop without requiring a human to issue each intermediate command \cite{jin2024llmagents, wang2025aiagenticprogrammingsurvey}. How to architect such systems for reliability, how to coordinate specialised agents without incurring prohibitive inter-agent communication overhead, and how to evaluate their outputs against standards that extend beyond task completion rate are now simultaneously open engineering and research problems. For software system design, this shift is not merely incremental---it represents a structural reorientation of the human--machine relationship that classical software architectures did not anticipate. Those architectures draw a sharp boundary between human intent and machine execution: the engineer specifies; the tool executes within tightly scoped preconditions. Agentic systems dissolve that boundary: a single agent, or a coordinated ensemble, can in principle traverse the entire software development lifecycle autonomously---eliciting and formalising requirements, synthesising and compiling code, executing regression suites, and performing static analysis---cycling through these phases in a planning-execution-reflection loop without requiring a human to issue each intermediate command \cite{jin2024llmagents, wang2025aiagenticprogrammingsurvey}. How to architect such systems for reliability, how to coordinate specialised agents without incurring prohibitive inter-agent communication overhead, and how to evaluate their outputs against standards that extend beyond task completion rate are now simultaneously open engineering and research problems. Understanding how to design, coordinate, and evaluate these systems is therefore one of the more pressing questions currently facing software engineering research and practice.
This survey interrogates how agentic AI systems are designed, evaluated, and coordinated, tracing four mutually reinforcing threads through the literature: foundational taxonomies that partition the design space between symbolic and neural paradigms; coordination mechanisms that emerge when multiple specialised agents are composed into teams; the empirical record of deploying agents across the software engineering lifecycle from requirements elicitation to post-deployment maintenance; and the internal planning, reasoning, and tool-use loops that determine whether an agent can sustain coherent behaviour over extended task horizons. A critical examination of persistent limitations---including hallucination propagation in multi-step execution, evaluation fragmentation across the lifecycle, and the near-absence of governance frameworks in published architectures---and a structured analysis of promising future directions conclude the review. This survey interrogates how agentic AI systems are designed, evaluated, and coordinated, tracing four mutually reinforcing threads through the literature: foundational taxonomies that partition the design space between symbolic and neural paradigms; coordination mechanisms that emerge when multiple specialised agents are composed into teams; the empirical record of deploying agents across the software engineering lifecycle from requirements elicitation to post-deployment maintenance; and the internal planning, reasoning, and tool-use loops that determine whether an agent can sustain coherent behaviour over extended task horizons. A critical examination of persistent limitations---including hallucination propagation in multi-step execution, evaluation fragmentation across the lifecycle, and the near-absence of governance frameworks in published architectures---and a structured analysis of promising future directions conclude the review.