Why Understanding the Full Agent Stack Is Critical
Building an AI agent isn’t just about plugging in an LLM and hoping for the best. True intelligence comes from how each layer, including planning, tools, memory, and evaluation, works together. Without that balance, even the smartest models can become unreliable or inconsistent.
When one layer fails, the system falters. A missing planner means poor task control; weak memory causes context loss; and no evaluation means unchecked hallucinations. Each layer reinforces the others like gears in a machine that must turn in sync.
That’s why modern AI development is shifting toward Agentic RAG, a concept IBM defines as “enhancing traditional retrieval systems with intelligent agents that plan, reason, and adapt.” This shift marks a new era where building smarter agents depends on understanding every layer of the stack, not just the model behind it.
The Planner / Orchestration Layer: Thinking Before Acting
Before an AI agent can act, it needs to think. The planner (or orchestration layer) serves as the agent’s brain - breaking tasks into smaller steps, deciding which tools to use, and coordinating subagents. It’s what turns a prompt into an intelligent plan instead of a random guess.
Breaking Down Tasks and Managing Subagents
The planner decides what needs to be done and who does it. In multi-agent systems, it delegates work to specialized subagents; one may search data, another may summarize results, and a third might verify accuracy. This orchestration ensures smooth collaboration and prevents overlapping actions.
Architectural Models of Planning
- Reactive agents act immediately on inputs - fast but less flexible.
- Deliberative agents pause to plan, reason, and decide - more accurate but slower.
- Hybrid agents blend both: they think strategically but still respond in real time.
Modern AI agent architectures often adopt this hybrid model, allowing agents to balance speed and reliability.
Tools and Frameworks for Agent Orchestration
- LangGraph - for visual, flow-based orchestration of multi-step agent logic.
- CrewAI - coordinates multi-agent collaboration for specific tasks.
- Autogen - by Microsoft, supports autonomous dialogue and planning among agents.
- MetaGPT - structures team-based agents that act like software organizations.
These tools let developers define how agents think, plan, and act - ensuring their workflows are not only automated, but intelligently coordinated.
The Tools / Action Layer: Connecting Agents to the World
If the planner is the brain, the tools layer is the body - it’s how AI agents interact with the real world. This layer connects the agent to APIs, browsers, databases, and enterprise systems, giving it the power to not just think, but do.
When designed right, tools turn passive intelligence into action - from sending emails to querying databases, summarizing reports, or triggering automation flows.
APIs, Browsers, and Connectors
The most common tools include API integrations, browser agents, and database connectors. APIs help agents talk to CRMs, ERPs, or ticketing systems; browsers enable data extraction; and connectors link to SQL or vector stores. Together, they expand what the agent can accomplish beyond its internal reasoning.
Security and Governance Controls
With power comes risk - and that’s why tool security and governance are crucial. Safe invocation policies, sandbox environments, and audit logs ensure the agent only performs authorized actions. These safeguards prevent data leaks, malicious commands, and unauthorized system access.
Dynamic Retrieval in Agentic RAG
In Agentic RAG, the tools layer becomes even smarter. Agents can dynamically choose between multiple retrieval sources - say, switching from Weaviate to Pinecone or using API-based vector queries when context changes. This adaptability lets agents pick the most relevant data pipeline on the fly, improving both accuracy and response time.
The tools layer, in essence, turns reasoning into real-world impact safely, efficiently, and intelligently.
The Memory / Knowledge Layer: Retaining Context & Long-Term Recall
Every smart agent needs a good memory. The memory layer helps AI agents remember context, learn from interactions, and recall facts when needed. Without it, even the best models forget earlier steps or repeat the same mistakes - like talking to someone who resets every five minutes.
Types of Memory: From Instant to Long-Term
AI agents use multiple kinds of memory, each serving a unique purpose:
- Short-term memory (scratchpad): Holds recent conversation history and reasoning steps.
- Working memory: Keeps relevant facts during multi-step reasoning or task execution.
- Long-term memory: Stores persistent knowledge in vector databases for future recall.
This hierarchy allows agents to combine fast reactions with deeper understanding.
Vector Embeddings and Databases
To store and retrieve knowledge efficiently, agents rely on vector embeddings, mathematical representations of meaning. Tools like Pinecone, Weaviate, and Chroma index these vectors so agents can “remember” semantically similar information.
When a new query arrives, the system fetches relevant chunks from the vector store - grounding the model’s output in real data instead of guesses.
Emerging Architectures for Smarter Memory
The future of agent memory goes beyond simple storage. Systems like SHIMI (Semantic Hierarchical Memory Integration) introduce layered, explainable recall, allowing agents to retrieve not just data but context behind that data. This leads to more transparent, scalable, and human-like reasoning - where agents remember both what happened and why it mattered.
The memory layer, then, isn’t just about storage - it’s about building continuity, learning, and trust into AI agents.
The Evaluation & Feedback Layer: Testing, Inspecting, Improving
Even the smartest AI agents need supervision. The evaluation layer keeps them accountable, testing how well they perform, how accurate their outputs are, and whether they actually follow instructions. Without evaluation, autonomy turns into unpredictability.
Why Evaluation Matters
Agents are non-deterministic.They don’t always give the same answer twice. Regular evaluation helps measure relevance, coherence, tool accuracy, and latency, so teams can track performance over time and catch issues early.
Tools for Agent Evaluation
Popular frameworks include:
- PromptLayer - tracks prompt versions and agent behavior.
- TruLens - monitors quality and interpretability of LLM outputs.
- Custom evaluation pipelines - built to test domain-specific logic or compliance.
With proper feedback loops learn, adapt, and improve continuously.
Top Frameworks & Tools to Build AI Agents in 2025
We’re in an evolving landscape of agentic AI. These frameworks each bring something unique like orchestration, memory, collaboration, or visual workflows. Below is a comparison of leading options:
| Framework / Tool | Strengths / Use Cases | Trade-offs / Weaknesses | Maturity & Ecosystem |
| LangChain | Highly modular; chains, memory, tool integration; wide adoption in the AI community. | Can get complex as projects grow; managing state across agents is trickier | Large community, many extensions and plugins available |
| Autogen (Microsoft) | Strong multi-agent orchestration; Microsoft support; good for collaborative agents | Steeper learning curve; heavier setup | Growing adoption in enterprise and research circles |
| CrewAI | Designed for role-based agents (analyst, strategist, etc.); clean multi-agent logic | Less mature tooling around memory or visual debugging | Niche but promising for team-based agent systems |
| Microsoft Semantic Kernel | Skill-based architecture; strong for enterprise workflows and integration | Not as focused on heavy agent orchestration; memory & chaining less emphasized | Backed by Microsoft, good for building reliable, production systems |
| Dify | Visual, low/ no-code agent platform; built-in RAG, orchestration, tool plugins | Less control at the lowest layers; may be limited for hyper-custom or complex backends | Rapidly evolving, good for prototyping and democratizing AI agent development |
Quick Takeaways
- LangChain remains a safe, flexible all-around choice for many agent projects.
- Autogen shines when multi-agent orchestration is central.
- CrewAI is great for structured role-based agent setups.
- Semantic Kernel suits enterprise use cases with solid integration.
- Dify is ideal when you want to build agents fast with visual tools and less boilerplate.
Real-World Implementations & Use Cases
AI agents are already reshaping how teams work - from support desks to enterprise data ops. Here’s how they show up in the real world:
Autonomous Support Assistants
Agents that pull answers from documentation, check ticket history, and even trigger actions like refunds - all without human help.
Agentic RAG Systems
Smarter RAG setups that plan what to retrieve and when, adapting to context for sharper, faster responses.
Industry Applications
- Legal: Contract review, clause checks.
- Customer Support: Multi-channel automation with human-like recall.
- Enterprise Search: Unified access to files, CRMs, and project data.
- Education: Personalized, memory-based learning bots.
Future Directions: Self-RAG, Agentic RAG, Multi-Agent Systems
The next wave of AI agent innovation is about smarter retrieval, coordination, and memory - agents that learn how to think better over time.
- Agentic RAG - Agents dynamically orchestrate retrieval, planning, and tool use, moving beyond rigid pipelines. Instead of following static RAG steps, they choose what to fetch, when to reason, and how to act - much like human researchers.
- MA-RAG (Multi-Agent RAG) - In these setups, specialized agents take on distinct roles: a retriever agent finds context, a planner decides next steps, and a QA agent verifies results. This division of labor boosts efficiency and reasoning depth.
- Self-RAG - Future systems will adapt retrieval strategies automatically. They’ll learn which data sources or chunking strategies work best, optimizing retrieval loops over time through feedback and experience.
- Advanced Memory Systems (SHIMI & Beyond) - New frameworks like SHIMI enable semantic and hierarchical memory, letting agents retain context for months, not minutes. This means deeper understanding, fewer resets, and more consistent long-term behavior.
The goal is self-improving ecosystems that evolve their reasoning, retrieval, and collaboration on their own.
Final Thoughts on Building Smarter, More Grounded Agents
Building AI agents is about finding balance. Every design choice is a trade-off between complexity, performance, and robustness. The smartest agents aren’t the ones that know everything, but the ones that learn and adapt with purpose.
Start small. Build a clear planner-to-memory loop, test retrieval carefully, and keep your evaluation layer active. Agents evolve best through iteration, feedback, and fine-tuning.
If you’re ready to design agents that truly use your data, reach out to RT Dynamic. We build end-to-end AI agent stacks, agentic RAG pipelines, and custom memory architectures that scale with your business and goals.
FAQs: Building AI Agents Tech Stack
What layers are needed to build AI agents?
A complete agent includes four key layers: planner, tools, memory, and evaluation. The planner decides what to do, tools execute actions, memory keeps context, and evaluation ensures ongoing improvement.
Do I always need retrieval?
Not always. Retrieval (RAG) helps when your agent needs external knowledge or dynamic data. But for closed, well-defined domains, a No-RAG setup with strong internal memory can be faster and simpler.
How do memory and retrieval differ?
Memory stores what the agent learns and reuses internally, while retrieval pulls fresh information from external sources like databases or documents. Memory remembers, retrieval re-discovers.
Can I evaluate agents reliably?
Yes. Frameworks like TruLens, PromptLayer, and custom pipelines can measure accuracy, coherence, and latency. Regular evaluation prevents drift and keeps agents aligned with goals.
What tools are best for AI agents in 2025?
Top frameworks include LangChain, Autogen, CrewAI, Microsoft Semantic Kernel, and Dify. For custom enterprise deployments, integration-focused partners like RT Dynamic help build scalable, production-ready agent ecosystems.
