Agentic AI 2025: Building the Stack for Autonomous, Trustworthy Agents
By Dr Luke Soon – Computer Scientist, AI Ethicist
Introduction
We stand at a pivotal moment in AI development. Recent advances in large language models, agent architectures, knowledge augmentation, and deployment infrastructure are converging to shift what we once considered “tools” into something closer to autonomous actors. To navigate this new terrain safely, effectively, and humanely, we need more than incremental progress — we need a structured roadmap.
In this post, I present Agentic AI 2025, a layered competency and tech stack model that charts the path from foundational skills to full production-grade agent systems. I will walk through each layer: what to know, what to prioritize, what tools are emerging, and what the key risks and governance issues are.
Layer 1: Programming & Prompting
What this is
This is the base layer: fluency in programming languages, automation, and the art of prompting. Without this, higher agentic capability is brittle.
Key Skills
Languages: Python remains essential. JavaScript / TypeScript often used for front-end interaction, tooling, wrappers. Shell / Bash useful for glue and dev ops.
Automation / Scripting: HTTP / JSON API requests; file I/O; asynchronous programming; web scraping.
Prompting:
Prompt engineering and context management.
Chain-of-Thought (CoT) prompting, where the agent reasons step-by-step rather than jumping to conclusions.
Role prompting, goal-oriented prompting, self-critique & retry loops (i.e. prompting the agent to evaluate and correct its own reasoning), reflexion, etc.
Why it matters
Agents are only as good as their prompting scaffolding. Poor prompts lead to hallucinations, unreliable behavior, misuse of tools, etc. By investing here, you dramatically improve agent reliability, interpretability, and adaptability.
Layer 2: Basics of AI Agents
What defines an agent
An AI agent is not just a fancy chatbot; it is something that can plan, act, adjust, and manage goals over time. Key differences include:
Autonomous vs Semi-Autonomous Agents: Some agents require human oversight; others operate with increasingly less supervision.
Goal Decomposition & Task Planning: Breaking a complex goal into smaller tasks; scheduling, sequencing.
Decision-Making Policies & Action Planning Loops: Not just reasoning, but choosing what to do, when, how.
Agent Architectures such as ReAct, CAMEL, AutoGPT, etc.
ReAct (Reasoning + Acting) is a paradigm that integrates reasoning (e.g. chain-of-thought) with action/tool usage.
CAMEL is an open-source multi-agent framework that emphasizes communicative agents, collaboration, roles, and memory.
Protocols / Interaction Patterns: Model Context Protocols (MCP), agent-to-agent communication (A2A).
Self-Reflection & Feedback Loops: Agents that monitor their own performance or adjust as new feedback arrives.
Layer 3: LLMs & APIs
Core components
Model providers: OpenAI’s GPT-4/GPT-4o, Anthropic Claude, Google Gemini, Mistral, open-source models like LLaMA, Falcon, DeepSeek, etc.
APIs: Function calling; tool invocation; output parsing; chain prompt via APIs; rate limiting; authentication.
These define the intelligence and the constraints. Choice of model affects cost, latency, capabilities, safety (e.g. whether one can inspect internal states, access logs), etc.
Layer 4: Tool Use & Integration
Agents are more useful when they can act in the world, not just respond in text. Key aspects:
Integration with tools: calculators, code interpreters, external APIs, web browsing.
Memory integration: short-term context, long-term storage.
File readers/writers, Python execution tools etc.
Bridging “reasoning” (LLMs) and “action” (tools) empowers agents to do research, automate workflow, perform remediation.
Layer 5: Agent Frameworks
These are middleware / scaffolding that make building agentic systems easier, more modular, safer.
LangChain: A highly modular, popular framework for building chains, agents, memory, etc.
LlamaIndex: More specialized for Retrieval-Augmented Generation (RAG) and knowledge indexing; simpler to get up and running for search / knowledge intensive apps.
CAMEL, AutoGen, Flowise, AgentOps, Haystack, Semantic Kernel, etc.
These enhance reusability, reduce boilerplate, help in observability, and often include guardrails / safety features.
Layer 6: Orchestration & Automation
When you go beyond single agents to systems of agents, or workflows with branching, triggers, etc.:
Event-based triggers, DAG (Directed Acyclic Graph) workflows, looping & conditional workflows.
Orchestration platforms: Zapier, n8n, Make.com. These allow low-code / no-code or “glue code” integrations.
Agent systems: coordination, role separation, plan merging, resource sharing.
These are needed to scale, to integrate with business workflows, to ensure reliability under load or complexity.
Layer 7: Memory Management
For agents to be “aware” over time:
Types of memory: short-term (recent context), long-term (persisted over sessions), episodic (events), etc.
Use of vector stores / vector databases like Pinecone, Weaviate, Chroma, FAISS. These allow embedding of past information for similarity / recall.
Memory enables continuity, personalization, learning, consistency of decisions over time.
Layer 8: Knowledge & RAG (Retrieval-Augmented Generation)
Crucial for grounding agents in up-to-date or domain-specific information, avoiding hallucination:
Indexing: embedding models + custom loaders + document splitters / chunkers.
Retrieval: fetch relevant documents from indexes / vector stores, hybrid search.
Augmentation: feeding retrieved documents into prompts; query refinement.
Generation: model leverages both its core abilities + external knowledge to respond.
Frameworks like LangChain and LlamaIndex are central here, each with trade-offs. LlamaIndex tends to make retrieval easier; LangChain gives more control over chains, agents, customization.
Layer 9: Deployment
Production matters. Key concerns:
Deployment modes: APIs; serverless functions; hosting services (Modal, Replit, etc.).
Application front end/tools: FastAPI, Streamlit, Gradio for interfaces.
Infrastructure: Docker, Kubernetes, vector DB hosting services.
Scaling: latency, throughput, faults, cost, region.
Layer 10: Monitoring & Evaluation
Safety, reliability, continuous improvement depend on feedback:
Metrics: success rate, error rates, latency, correctness, user satisfaction.
Human-in-the-loop feedback: user feedback, oversight.
Observability: Logging, tracing, dashboards (e.g. Grafana, Prometheus).
Auto-evaluation loops: building systems that test themselves; OpenTelemetry etc.
Layer 11: Security & Governance
Often under-appreciated until something goes wrong. Must be embedded from Day One.
• Prompt injection protection.
• API key management.
• Authentication & Role-Based Access Control (RBAC).
• Output filtering, moderation.
• Red-team testing.
• Data Privacy & Compliance (GDPR, HIPAA, etc.).
Emerging Architectures & Research Directions
While the stack above covers much of what is being deployed now, some research frontiers are pushing things further:
ReflAct: an architecture that builds on ReAct by adding goal-state reflection, reducing drift from goal during execution. Early results show it improves alignment and reduces incoherence.
Autono: a framework designed for more robust autonomy, dynamic decision making, probabilistic strategies (for abandonment or fallback), memory transfer among multi-agent systems, etc.
These promising trends suggest that agentic systems will become both more capable and more reliable over time.
Trade-offs, Risks & Design Considerations
As someone deeply interested in human experience, ethics, and social impact, I believe the following trade-offs are essential to weigh:
Autonomy vs Control More autonomous agents are powerful—but also risk unintended consequences. Human-in-the-loop or oversight loops are crucial.
Cost vs Performance Powerful LLMs + context windows + tool integrations + memory = high compute, latency, monetary cost. Optimization is essential.
Transparency vs Complexity As agents become more complex (multi-agent, learning, memory, chaining), their behavior becomes less interpretable. How do we make them auditable?
Bias / Safety / Hallucination Grounding with RAG helps. Prompt engineering, output filtering, red teaming help.
Privacy & Ethics Data used in memory, retrieval, indexing may include private or sensitive information. Data governance is critical.
My Position: Human-Centered Agentic AI
In my work (Genesis: HX = CX + EX), I argue that agentic AI must be designed around human experience (HX), which integrates both customer experience (CX) and employee/creator experience (EX). Every layer of this stack must be evaluated in terms of how it affects people: trust, empowerment, fairness, wellbeing.
Agents should augment human capacity, not replace human dignity.
Transparency, explainability, feedback are not optional.
Governance must be proactive, not reactive.
Conclusion
Agentic AI 2025 is not just about building powerful agents; it’s about building responsible, trustworthy, useful agents that work with humans, not merely for or in place of them.
If you are investing in AI today — skill-building, infrastructure, policies — I encourage you to map your current strengths against the layers above. Where are your gaps? Which tools or frameworks can help you close them? How are you ensuring your agents are aligned with your mission and society’s values?
In subsequent posts, I’ll provide case studies, architectures in code (LangChain + CAMEL + ReflAct), and governance templates to help organisations adopt this roadmap.
References
“ReAct: Synergizing Reasoning and Acting in Language Models,” on integrating reasoning & action in LLM agents.
LangChain vs LlamaIndex: differences in modularity, control, retrieval-intensive applications.
CAMEL AI framework: building multi-agent systems, communication, memory, agent roles.
ReflAct: world-grounded decision making via goal-state reflection.
Autono: more robust autonomous agent framework.