TL;DR: What you’ll learn
- 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;
- Workflow Tier – prompt chaining, routing, evaluation, orchestration;
- 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?
