Agentic AI

What it is

Agentic AI describes intelligent systems that act autonomously: they perceive an environment, set or accept goals, plan sequences of actions, make decisions, and execute tasks with little or no human intervention. Modern agentic systems combine planners, reinforcement- or policy-learners, memory modules, perception (vision/sensors), and language or tool-use layers so an agent can both reason and act over extended time horizons. These agents do more than answer queries — they pursue objectives, monitor outcomes, recover from failures, and adapt behavior as situations change. Recent surveys and reviews treat agentic systems as a distinct research area that spans planning, multi-agent coordination, continual learning, and safe/robust control.

Applications

Agentic AI already appears in several deployment areas (representative examples):

• Autonomous drones & UAV fleets — agents sense conditions, reassign tasks, and re-route dynamically for mapping, inspection, or disaster response.
• Robotic process automation (RPA) with reasoning) — agents plan which business processes to automate, choose strategies, and handle exceptions rather than following fixed scripts.
• Self-managing cloud infrastructure — agents monitor loads, spin up/down resources, and perform automated incident mitigation.
• Adaptive supply-chain systems — agents forecast demand, negotiate routes, and reconfigure inventories in response to disruptions.

These are illustrative; agentic capabilities also extend to healthcare monitoring, finance (autonomous trading systems), and smart-city management.

Future potential

As agents grow more capable, we can expect them to act as coordinators of multiple subsystems (agent-of-agents), negotiate contracts with humans or other agents, and perform real-time optimization of complex networks (traffic, energy, logistics). Realizing that potential will require advances in safety, interpretability, legal frameworks, and standards for auditing agent behavior. Several recent surveys and review articles emphasize both the technical promise and the urgent need for governance and alignment research.

Current research areas under Agentic AI

1. Autonomous decision-making & planning architectures
   Work focuses on hierarchical planning, model-based vs model-free control, goal decomposition, and integrating symbolic planning with learned policies. Researchers study how agents set subgoals, reason about consequences, and reconcile short- vs long-term objectives.
2. Multi-agent coordination, negotiation & emergent behavior
   This includes cooperative task allocation, market/auction mechanisms for resource sharing, communication protocols, and methods to avoid negative emergent effects (e.g., oscillations or resource collapse) when many agents interact.
3. Continual learning, adaptation & robustness
   Agents must learn from ongoing experience, adapt to non-stationary environments, and avoid catastrophic forgetting. Research covers lifelong learning, domain adaptation, online RL, and safe exploration.
4. Explainability, verification & alignment
   As agents take actions with real consequences, we need interpretable decision traces, verification techniques for safety constraints, and methods to align agent objectives with human values. This area blends AI, formal methods, and human-computer interaction.
5. Security, adversarial resilience & governance
   Research examines adversarial attacks on agent perception/planning, secure coordination in multi-agent setups, and policy/standards for liability, audit trails, and responsible deployments.
6. Systems & integration: tools, middleware, and benchmarks
   Building reliable agentic systems requires system engineering work: memory/storage patterns for long-lived agents, tool interface standards, simulation environments, and reproducible benchmarks for agentic behaviors.

(These areas map to current conference/workshop topics and recent literature surveyed in 2024–2025 reviews.)

Key journals that publish Agentic AI work

Below I list at least 3 journals per access model, with indexing/status notes (Scopus/CiteScore/SJR where available). You asked for Scopus and “CSI Tools” — I read that as a reference to journal-metrics tools such as Scopus/CiteScore and Scimago (SJR); I therefore include Scopus indexing and CiteScore/SJR references where available. If you meant a different “CSI Tools,” tell me and I’ll adapt.

Open access (fully OA; usually APC for authors)

1. Journal of Artificial Intelligence Research (JAIR) — Open access. Broad AI journal (agents, planning, RL). Indexed widely (Scopus/Clarivate). Good for theory + systems.
2. AI (MDPI) — Open access (MDPI) — covers planning, learning, robotics, multi-agent topics; indexed in Scopus (accepted into Scopus in 2023). APC applies.
3. Frontiers in Robotics and AI — Fully open access (Frontiers). Accepts agentic/robotics research; indexed (Scopus, Scimago). APC applies.

Hybrid (subscription journals that offer optional OA)

1. Autonomous Agents and Multi-Agent Systems (Springer) — Hybrid. Core outlet for agentic and multi-agent research; indexed in Scopus and SJR. Use this for focused agent architectures and theoretical work.
2. ACM Transactions on Autonomous and Adaptive Systems (TAAS) — Hybrid. Publishes autonomous/adaptive systems research; indexed by Scopus. Good for systems and engineering angles
3. IEEE Transactions on Robotics / IEEE Transactions on Intelligent Vehicles — Hybrid (IEEE offers OA option). Strong venue for agents applied to robotics, vehicles, and intelligent systems; indexed in Scopus/WoS.

Subscription / paywalled

1. Artificial Intelligence (Elsevier, AIJ) — Traditionally subscription with hybrid/delayed OA options. Top-tier AI journal that covers multi-agent systems, planning, and foundations. Indexed in Scopus and Web of Science; high selectivity.
2. IEEE Transactions on Neural Networks and Learning Systems (TNNLS) — Subscription with OA option. Publishes algorithmic advances relevant to learning agents; indexed in Scopus.
3. Multiagent and Grid Systems (SAGE/Elsevier platforms) — Subscription/hybrid depending on publisher policies. Focuses on multi-agent architectures, distributed systems, and grid/cloud agentic behaviors. Indexed in Scopus/Scimago.

Quick guidance on choosing where to submit

• For fast open visibility (and you accept APCs): JAIR, MDPI AI, and Frontiers in Robotics and AI are good choices; they index in Scopus and surface quickly to search engines.
• For top-tier theoretical impact, submit to Artificial Intelligence (Elsevier) or Autonomous Agents and Multi-Agent Systems (Springer) — they have high selectivity and established reputations.
• If your work is robotics/vehicle-focused, target IEEE T-RO or IEEE Transactions on Intelligent Vehicles which combine systems rigor with practical evaluation.

Notes on metrics & the “CSI Tools” phrase

• Scopus / CiteScore: Scopus provides indexing and CiteScore journal metrics (Elsevier). CiteScore is commonly used to compare journals by citation impact; you can check a journal’s CiteScore on Scopus/Elsevier pages.
• Scimago Journal Rank (SJR): SJR and quartile info are useful complements (Scimago/Scopus data). Many of the journals above appear in Scimago/Q rankings (I cited SJR/Scimago pages where available).

If by “CSI Tools” you meant a specific platform or institutional tool, tell me its exact name and I’ll fetch how it evaluates those journals. (I searched common terms — “CSI Tools” more often refers to forensic/enterprise tools or to unrelated CSI acronyms, so I interpreted your intent as journal-metrics tools like CiteScore/SJR.)

Would you like a downloadable table (CSV/Excel) listing these journals, their access model, Scopus indexing status, and common APC ranges — so you can sort/filter by field and cost? I can produce that instantly.