AI Agent Systems: Planning, Tool Calling, and Memory Design Training

Detailed Content (EN)

This training is designed to help companies build agent systems not as eye-catching technology demos, but as real systems that execute workflows, connect to tools, plan step by step, obtain human approval when necessary, operate safely, and remain observable in production. At the center of the program is one core idea: a strong agent system is not merely a model that produces the right answer; it is a working system that selects the right problem, decomposes tasks correctly, uses the right tools at the right time, manages memory in a controlled way, hands off to humans at critical points, and makes every step measurable.

Throughout the training, participants learn to distinguish where agent systems are truly necessary and where they merely introduce unnecessary complexity. They see that not every use case needs an agent; some problems are better solved with deterministic workflows, some with RAG, some with tool-using assistants, and some with true planning agents. For that reason, the program centers not on “let’s build an agent,” but on the question “what level of autonomy is appropriate for which problem?”

The first strong pillar of the program is the planning and orchestration layer. Participants learn how an agent should interpret a task, break it into sub-tasks, decide when to plan, decide when to update a plan, determine which steps require validation, and apply the principle of bounded autonomy. In addition, orchestration is not treated as merely a technical chaining mechanism, but as an architectural decision that carries security, quality, and workflow control implications. This gives participants an engineering perspective that allows them to choose consciously among single-agent, multi-tool, multi-agent, and human-in-the-loop hybrid designs.

The second strong pillar of the program is the tool-calling layer. Participants systematically address tool definition, function-schema design, input-output contract discipline, tool routing, retries, fallbacks, approval gates, permission scopes, and execution safety. In particular, they see that the success of agent systems in production often depends more on how well tools are designed and invoked than on the model itself. Through practical examples, they learn how poor tool descriptions, overlapping tool domains, weak parameter structures, and ambiguous return formats reduce agent quality.

The third major axis of the program is memory design. Participants distinguish short-term context, session memory, long-term memory, episodic memory, semantic memory, and enterprise user history. They see that not every memory type is necessary for every use case, that memory brings risks as well as benefits, and that poorly designed memory layers can create cost, privacy issues, error accumulation, and loss of control. In this way, the training teaches memory not as a magical feature, but as a system decision that must be managed carefully.

Another critical axis is evaluation, observability, and production readiness. Participants learn how to design step success, task success, tool-selection accuracy, planning quality, failure-mode analysis, regression risk controls, traceability, run logs, and approval visibility for agent systems. As a result, systems can be assessed not only on whether they run, but on whether they are reliable, governable, and operationally sound.

The final major topic is security and governance. The training addresses secure agent design through tool abuse, prompt injection, privilege escalation, data leakage, unsafe execution, over-autonomy, and lack of auditability. As a result, the program aims not only to teach how to build agents that act, but how to make them defensible and governable at enterprise scale.

Who Is This For?

• AI engineers, ML engineers, applied AI teams, and agentic AI teams
• Backend, platform, and product-development teams
• Technical teams building tool-using LLM systems, agent solutions, or intelligent assistants
• Digital transformation, innovation, and AI product teams
• Companies building AI solutions integrated with CRM, ERP, ticketing, document systems, and internal APIs
• Technical leads and architects aiming to move agent projects from prototype to production

Highlights (Methodology)

• An advanced structure that combines planning, tool calling, memory, evaluation, security, and production readiness in one program
• An approach focused on problem-solution fit, bounded autonomy, and architectural decision-making rather than simple framework exposure
• Real enterprise use cases, workflow scenarios, and tool-integrated system design exercises
• A methodology that systematically addresses function schemas, tool contracts, routing, approval gates, and fallback logic
• An approach that treats memory not as technical novelty, but through the lens of control, quality, and risk management
• A learning model suited to producing reusable prompt, tool, memory, evaluation, and control templates within teams

Learning Gains

• Select the right agent, workflow, or assistant pattern for enterprise problems
• Design planning and orchestration logic according to the use case
• Build more reliable, controlled, and production-ready tool-calling layers
• Design memory strategies with a benefit-risk balance
• Make agent-system quality sustainable through evaluation and observability
• Develop secure, governable, and enterprise-defensible agent systems

Frequently Asked Questions

• Is this training suitable for beginners? No. This is an advanced program. Participants are expected to have awareness of Python, API logic, basic backend concepts, and LLM applications.
• Does this training only teach a specific agent framework? No. The content can be designed framework-agnostic. However, it can also be tailored with technologies such as LangGraph, LangChain, MCP, and API-orchestration layers.
• Is this training only for building chatbots? No. The training is designed for enterprise agent systems that run workflows, use tools, make decisions, and operate with approval mechanisms.
• Can it be customized with institution-specific tools, data, and processes? Yes. The content can be tailored based on the institution’s system landscape, integration needs, security level, process complexity, AI maturity, and target use cases.