Agentic AI, also called agentic artificial intelligence, describes systems that don’t just generate content; they plan, decide and act towards goals with minimal supervision. That shift matters. It moves AI from a helpful assistant to an autonomous teammate.
In 2025, adoption is rising, but value depends on execution. Recent enterprise research by Accenture shows many firms are scaling solutions while only a minority create significant, organisation-wide impact; the stand-outs invest in the operating model, not just the model itself. You’ll learn what agentic AI is, how it differs from traditional approaches, the core capabilities, risks, and a safe, step-by-step way to get started, plus how Found can help.
Understanding agentic AI: clear definition & why it matters
Agentic AI systems combine reasoning, memory and tool-use to decompose objectives into tasks, execute them, and adapt from outcomes. ArXiv taxonomy distinguishes conventional “AI agents” from agentic AI by emphasising persistent memory, dynamic task decomposition, orchestrated autonomy and multi-agent collaboration.
Why does it matters now?
Autonomous agents can reduce manual orchestration across research, content, experimentation and reporting, while creating new governance and safety requirements.
How is agentic AI different from generative AI or “just an LLM”?
The short answer is, generative AI produces outputs; agentic AI turns outputs into action.
- Generative models: predict text/code/images based on prompts.
- Agentic systems: set sub-goals, call tools/APIs, read/write memory, coordinate with other agents, and report back on progress – often without continuous human input.
What the data suggests: firms delivering enterprise-level value are significantly more likely to invest in agentic architectures and the surrounding operating model. The implication: success is less about a single model, more about process, governance and people.
Agentic AI is a helping hand
Agentic AI doesn’t replace strategy, it expands the scope of human creativity, letting teams focus on intent, innovation and impact. The brands that win blend technical excellence with authentic human insight, not automation for its own sake.
Key capabilities & components (and how to build them)
Frame the solution space: start small, wire the foundations, then expand.
What core capabilities define agentic AI?
- Goal decomposition & planning: break a brief into actionable steps; update the plan as new information arrives.
- Tool-use & integration: call APIs (analytics, CMS, ad platforms), write to docs, schedule tasks.
- Memory & context: store facts, decisions and outcomes for continuity across sessions.
- Multi-agent collaboration: specialised agents (researcher, strategist, optimiser) share a workspace to accelerate delivery.
How to implement safely: a practical sequence
Here’s a few key pointers that can help you along the way:
- Choose a bounded, high-signal use case. Start with keyword research, content briefs or CRO test setup.
- Wire data & memory. Connect read-only analytics and export a scoped memory store (e.g., vector DB) for facts and decisions.
- Define guardrails. Policy prompts, allow/deny tool lists, rate limits and spending caps.
- Pilot with human-in-the-loop. Review actions and outputs before execution; tighten or loosen autonomy as confidence grows.
- Instrument everything. Logs, traceability, and evaluation metrics (accuracy, safety, outcome lift).
- Harden security. Threat-model perception, reasoning, action and memory surfaces using community guidance.
Common pitfalls to avoid
- Unbounded autonomy. Agents “going wide” without limits can generate cost/risk quickly, set scopes and kill-switches.
- Thin data foundations. Poor taxonomy, entity gaps and siloed data undermine planning; invest in entities and structured data.
- Process neglect. Without change management and roles, pilots stall. Put humans at key decision points early.
For discovery performance in AI-led answers, pair agentic workflows with GEO best practices.
Why it matters to marketers & digital leaders
Agentic AI is a strategic inflexion point. We’ve seen the best outcomes where teams combine automation with human judgment.
- Our perspective is simple: AI is strategy-amplifying. It frees people to work on customer intent, creative leaps and experimentation cadence.
- Winning brands couple engineering discipline (data, guardrails, instrumentation) with human insight (brand voice, ethics, context).
- Practical outcomes we’re helping teams explore: autonomous campaign research, always-on testing, cross-channel pacing, entity upkeep, and proactive issue detection.
Agentic AI is the next step in applied intelligence – turning prompts into outcomes. Start small, build memory and guardrails, measure relentlessly, and scale what works. If you want a pragmatic roadmap, our SEO and AI SEO teams can help you design the pilot and land it in production. Get in touch with us here.
