> Markdown version of [/videos/1629-beyond-chatbots-how-to-build-agentic-ai-systems?t=1121](https://www.wearedevelopers.com/videos/1629-beyond-chatbots-how-to-build-agentic-ai-systems?t=1121). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# Beyond Chatbots: How to build Agentic AI systems

Stop building passive chatbots and start engineering autonomous AI. Learn to design self-correcting, multi-agent architectures that reason, plan, and execute complex workflows.

- **Speakers:** [Philipp Schmid](https://www.wearedevelopers.com/@philipp-schmid)
- **Event:** World Congress 2025
- **Published:** August 20, 2025
- **Duration:** 24:22
- **URL:** https://www.wearedevelopers.com/videos/1629-beyond-chatbots-how-to-build-agentic-ai-systems

## Summary

The evolution of AI development is rapidly shifting from instruction-following conversational models toward dynamic, autonomous agentic systems. Unlike a deterministic workflow guided by predefined computational steps, an agentic system relies on an LLM as its core reasoning engine to autonomously dictate application control flow based on situational inputs. This modern architecture bridges the gap between passive text generation and actionable execution through systems equipped with short- and long-term contextual memory, alongside specialized toolsets invoked via structured JSON schemas.<br><br>To scale these interactive models effectively, software teams can compose distinct agentic design patterns. The "reflection" pattern forces an LLM to self-critique and iteratively refine its own output, while "planning and orchestration" workflows utilize a primary planner model to break complex queries into manageable sub-tasks routed to localized worker nodes. For enterprise software, standardizing onto a "multi-agent" ecosystem—where dedicated personas interact collaboratively, functioning much like isolated microservices—drastically improves execution transparency and error tracing.<br><br>As AI agents increasingly interact with external environments and APIs, engineering focus must transition from simple prompt engineering to "context engineering"—ensuring the correct situational data is injected precisely at execution time. Testing and deployment methodologies must enact a similar pivot: instead of merely benchmarking capability on a singular run, developers must optimize for continuous system reliability to ensure multi-step logic succeeds without trapping the AI in infinite, resource-intensive operational loops. Supported by tooling such as LangGraph and multi-agent libraries, developing stable, self-correcting automation logic has become an accessible necessity for product developers.

**Keywords:** agentic AI systems, LLM control flow, context engineering, agent reflection pattern, AI workflow orchestration, JSON tool calling, multi-agent architecture, system reliability metrics, prompt context injection, langgraph integration, LLM reasoning loops, gemini CLI tooling, autonomous software workflows, AI persona routing

## Chapters

1. **Evolution of AI models into autonomous agents** (00:05) — The shift from language model text completion to instruction following and dynamic reasoning loops.
1. **Defining agents versus traditional programmatic workflows** (04:34) — How agents leverage language models to dynamically decide application control flows instead of following predefined sequences.
1. **Architectural patterns for composing dynamic AI agents** (06:50) — Combining design structures like reflection, tool use, planning orchestration, and multi-agent personas for complex tasks.
1. **Evaluating agent reliability and execution success rates** (12:27) — Shifting evaluation frameworks to measure execution cost, pass rate reliability, and safeguard against infinite execution loops.
1. **Context engineering for dynamic model reasoning** (15:59) — Structuring system instructions, user preferences, and tools dynamically to optimize an agent's reasoning conditions.
1. **Building practical AI agents using Google Gemini** (18:41) — Practical implementations using Gemini APIs, native SDKs, and open-source frameworks like LangChain and CrewAI.
1. **Future predictions for autonomous agent environments and robotics** (22:45) — How optimized hardware, critical data engineering, and reasoning models will accelerate personalized software and robotics access.

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