What exactly defines Agentic AI, and why it’s the leap beyond GenAI or RPA.
How to design goal‑directed agents that plan, reason, act, and adapt across complex enterprise workflows.
Strategies for contextual memory—enabling agents to retain relevant business context without hallucinating.
Best practices for trustworthy, scalable deployments: governance, oversight, explainability, real-time readiness, and cost management.
What is Agentic AI?
Agentic AI goes far beyond static chatbots or rule‑based automation. It’s built for goal-oriented, autonomous behavior—systems that:
Plan tasks,
Use closed-loop feedback to evaluate and adjust actions,
Reason over changing contexts,
Collaborate across tools and workflows.
As Christian Capdeville (Data IQ) puts it: “LLM‑powered systems that take action after generating answers.” These agents operate not as assistants, but as active team members in business workflows.
Designing Goal-Directed Agents in Dynamic Environments
Panelists emphasized:
Begin with the business problem first, not the tech: identify repeatable tasks with clear objectives before crafting agents.
Use closed-loop feedback (think thermostat-style adjustment): agents evaluate their work and self-correct over time.
Clearly scope agent domains to stay efficient—start with 10–20 well-defined use cases, don’t attempt enterprise generalists.
Contextual Memory: The Heart of Intelligent Agents
Without memory, even powerful agents feel amnesic. Use cases require:
Buffer memory for short-term context,
Summarization memory for long interactions,
Vector-based memory (e.g. RAG with embeddings) for knowledge retrieval.
Ganesh Jagadeesan and others recommend RAG as more than a retrieval pattern—it becomes a dynamic cognitive enhancer, keeping agents grounded in enterprise knowledge bases, compliance rules, and SOPs.
Reliability, Trust, and Governance
Key factors to build trustworthy agents:
Instrument agents as systems: log decisions, track sources, monitor performance over time.
Use semantic layers or data contracts to govern access—this avoids direct LM-to-data queries and keeps operations auditable and predictable.
Adopt governance frameworks like NIST AI RMF, IEEE or OECD standards to carefully balance autonomy and oversight.
Real-Time Data & Scalability Considerations
Segment use cases: analytical (low-risk questions), operational (agentic retrieval / real-time), and avoid full autonomy in critical workflows unless extremely controlled.
Technically, latency & cost scale with complexity—keep the architecture simple and scoped; layer agents on top of existing infrastructure (e.g. platforms already handling real‑time data).
Agentic Architecture: A Three-Tier Framework
As described by Subash Natarajan:
Foundation Tier – governance, source control, transparency before autonomy;
Autonomous Tier – constrained autonomy zones with checkpoints and fallback loops.
This phased approach lets teams begin safely and build trust before extending agentic capabilities widely.
⚡ Lightning-Round Advice from the Panel
Amay (Nexla): Embrace co-agency: involve humans from day one in agent workflows.
Jonathan (Elation/Alation): Prioritize trustworthiness—data lineage, provenance, clarity.
Ryan (Zenlytic): Think small and bounded—solve clear, narrow problems before scaling.
Christian (Data IQ): Don’t reinvent wheels—if your org has existing governance, compliance or workflows, layer agents on top rather than building from scratch.
Questions answered in this session
What makes Agentic AI different from GenAI or RPA?
How do you design agents that plan and reason over noisy enterprise workflows?
What memory architectures—buffer, summarization, vector—are necessary for agents?
How can we ensure agents stay reliable, predictable, and compliant?
What infrastructure patterns support real‑time production use cases?
How should enterprises scale gradually from pilot to agentic adoption?
