AI has moved from experiment to essential in just a few years. Large language models (LLMs) like GPT and Claude are now woven into the tools we use every day.

The first wave was about what these models could do: generate content, summarize information, answer questions. The second wave is about how we design systems around them — how we build AI that is reliable, accountable, and integrated with real business processes.

This is the age of agentic AI. Instead of isolated chatbots, we’re seeing networks of intelligent agents: software entities that remember, plan, act, and even collaborate with each other and with humans to get complex work done.

The implications are profound. But like any transformation, the challenge is as much about architecture and governance as it is about algorithms.

Memory Sits at the Core of Agency

Without memory, an AI system is stuck in the moment. It can respond, but it can’t adapt or learn. Modern agent design requires multiple layers of memory:

• Short-term context – the immediate conversation or task at hand.
• Persistent knowledge – facts about users, systems, and policies.
• Selective recall – deciding what matters most at the right time.

The design challenge isn’t just about the storage of memory data, but also mostly around choosing what to surface and when. Too much information clutters outputs and drives up cost. Too little, and the system loses continuity.

The most advanced agents now use semantic recall — retrieving memories based on meaning, not just recency — similar to how humans recall relevant experiences when solving a problem.

Smarter Models, Smarter Behavior

New reasoning models go beyond text generation. They break problems down step-by-step, showing their work along the way. This unlocks use cases like:

• Diagnostics and troubleshooting.
• Complex planning across multiple workflows.
• Compliance and risk assessment.

Instead of blindly producing output, these systems think through decisions and can explain their reasoning.

Dynamic, Adaptive Agents

Today’s static agents are limited: fixed prompts, fixed tools, fixed behaviors. Real work is dynamic. Next-generation agents are beginning to switch models based on task complexity, adjust tone and style in real time, and load and unload tools as situations change.

This flexibility adds complexity. Teams must plan for security, permissions, and testing across many possible configurations.

Rising Interoperability Standards

Fragmentation is a major barrier to scaling AI. The Model Context Protocol (MCP) is emerging as a common way to connect agents and tools. MCP makes it easier for different systems to work together. This speeds development and creates a foundation for broader ecosystems, where agents from different companies can collaborate securely. In addition to MCP, we’re actively helping customers complementary protocols, like agent-to-agent (A2A) and agent network protocol (ANP).

Evolving Context Retrieval Techniques

Retrieval-Augmented Generation (RAG) started off as one of the more common patterns for bringing enterprise data into AI systems. Newer agentic architectures are exploring non-RAG approaches that may be either simpler, or more sophisticated. Some of these include:

• Agentic RAG – agents call domain-specific tools instead of just retrieving text.
• Reasoning-Augmented Generation (ReAG) – models preprocess and structure data before retrieval.
• Full context loading – with massive context windows, entire datasets can be loaded directly.

The right strategy depends on your problem space. Start simple, then layer in complexity as needed.

Teams of Agents

The future isn’t a single all-knowing agent. It’s a team of specialized agents, each with defined roles. For example, instead of having a Sales agent that does “everything sales”, a company may instead define:

• A discovery agent handles communication with the customer
• An architect agent that generates a solution brief
• An estimation agent that creates a project estimate
• A proposal agent that creates a proposal
• An SOW agent that creates a contract
• A review agent checks for compliance.

New standards like A2A (Agent-to-Agent) are emerging to support collaboration between these systems, even across company boundaries.

Building these digital teams requires the same care as designing human ones — clear roles, rules of engagement, and oversight.

Observing and Measuring Agent Performance

As autonomy increases, visibility becomes critical.
Two practices are essential:

• Tracing every step, tool call, and decision for accountability.
• Continuous evaluation to measure quality, reduce hallucinations, and track performance over time.

This is the AI equivalent of DevOps: monitoring and improving systems continuously.

How to Prepare

For organizations exploring agentic AI, preparation should be tactical and deliberate:

1. Create architectural standards
   Define how agents will be built, secured, and integrated — similar to how you design API or security standards.
2. Develop an Agentic AI design guide
   Document patterns for creating new agents, including naming conventions, memory design, and tool selection.
3. Define measurement and governance
   Decide how you’ll trace behavior, run evaluations, and manage performance over time.
4. Pilot with clear boundaries
   Start with a focused use case, test extensively, and scale once the system is predictable and explainable.
5. Treat agents like team members
   Assign roles and permissions, set rules for collaboration, and review performance regularly.

Get Going!

Agentic AI represents a fundamental shift in how organizations operate. The next generation of systems won’t just respond to commands — they’ll plan, adapt, and collaborate. By focusing on memory, adaptability, standardization, and governance, businesses can move beyond experiments and build systems that are both powerful and trustworthy. The future of AI isn’t just smarter models. It’s smarter systems. Now is the time to start designing them.