A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond

Agent research is moving fast and spans RL, LLMs, cognitive science and multi-agent systems. Existing reviews tend to assume prior knowledge, so there are few simple frameworks for someone entering the field.

• A systematic literature review following PRISMA guidelines: Google Scholar searches, screening by title and abstract, then full-text review.
• Excluded papers without full-text access, papers only tangential to agents, and non-English work.
• Organized the results into architecture, applications across six domains, design directions, and a step-by-step guide for new researchers.

• A modern agent comes down to four parts: memory (short-term context plus long-term storage), tools (search, code interpreters, calculators), planning (chain-of-thought, reflection, breaking goals into subgoals) and actions.
• Self-improvement methods like Reflexion, which learns from verbal self-feedback, and Chain of Hindsight, which trains on past outputs paired with feedback, let agents improve without heavy fine-tuning.
• Agents are already used in healthcare (diagnostic support, patient chatbots), business (customer service, supply chains, fraud detection, trading), education (co-learners, mentor agents), science (automated labs, "AI Scientist"), public services, and games.
• Promising design directions are hybrid symbolic-plus-neural architectures, for interpretability and generalization, and hierarchical, modular designs that break big tasks into specialized sub-modules that are easier to debug.
• For newcomers: build the theory first (RL, planning, multi-agent coordination), pick projects with measurable outcomes, practice in simulators like CARLA and PettingZoo, and reproduce published results.

• Open challenges: robustness under distribution shift and adversarial inputs, opaque decision-making, bias and accountability, poor transfer to new tasks, and high compute and energy cost.
• Autonomy makes responsibility harder to pin down than with a plain LLM, because an agent's behavior keeps changing and it acts directly on people and systems.
• As a survey it summarizes others' results instead of running its own experiments, and the search was limited to open-access English papers.