# AI Agent Systems: Planning, Tool Calling, and Memory Design Training > Source: https://sukruyusufkaya.com/en/training/ai-agent-sistemleri-planning-tool-calling-ve-memory-tasarimi-egitimi > Updated: 2026-06-14T15:14:02.951Z > Level: advanced > Topics: AI Agent Sistemleri, Agentic AI, Planning, Task Decomposition, Tool Calling, Function Calling, Tool Routing, Workflow Orchestration, Memory Design, Session Memory, Long-Term Memory, Human-in-the-Loop, Approval Flow, Evaluation Engineering, Observability, AI Security, Prompt Injection, Tool Abuse, Governance, Production Readiness **TLDR:** An advanced AI agent systems training for enterprises covering planning, tool calling, memory strategy, orchestration, evaluation, security, and production readiness together. ## Açıklama AI Agent Systems: Planning, Tool Calling, and Memory Design Training is an advanced and intensive program designed not merely to help companies build question-answering chatbots, but to enable them to design enterprise agent systems capable of running real workflows through multi-step reasoning, tool use, task planning, memory management, human approval, security controls, and observable production-grade operating principles. Rather than approaching agents superficially as “LLM + tools,” the program presents a holistic enterprise AI engineering perspective covering task decomposition, bounded autonomy, orchestration, approval design, memory strategy, evaluation engineering, observability, security, and governance together. Throughout the program, participants systematically learn in which classes of problems agent systems truly create value, when classical workflow automation or retrieval-based assistants may be more appropriate, how tool-calling architectures should be designed, why function schemas and tool-contract quality directly affect agent performance, how to build planning and replanning patterns, how to distinguish short-term, long-term, and episodic memory approaches, the difference between session continuity and persistent memory, and how to design error handling, retries, fallbacks, and approval flows in multi-step agent systems. The program also covers evaluation and regression-test design for agents, tracing and observability for production visibility, and security risks such as prompt injection, tool abuse, privilege escalation, and data leakage, along with how to address secure agent design at enterprise scale. This program addresses a critical need: companies are moving beyond assistants that merely provide information and toward systems that actually perform work. They want to build agent solutions integrated with CRMs, ticketing tools, ERPs, document systems, data sources, internal APIs, and workflow applications; however, they often struggle to move into production due to weak tool-selection logic, poor planning design, unclear memory boundaries, incorrect tool invocation, uncontrolled autonomy, low observability, security gaps, and lack of quality measurement. The program focuses exactly on this transition point and teaches the technical decision logic that moves agent systems from “impressive demos” to “enterprise-manageable and defensible systems.” A major differentiator of the program is that it treats agent design not merely as intelligent response generation, but as decision-and-action architecture. Participants see that the success of a strong agent system is determined not only by model capability, but by task-decomposition quality, tool-contract discipline, memory-scope control, correct placement of human-in-the-loop checkpoints, tool-selection reliability, step-validation mechanisms, traceability, and safe-execution boundaries. For that reason, the training focuses not only on what the model says, but on when the system thinks, when it uses tools, what it remembers, what it should forget, when it should hand off to a human, and how each step should be observed. By the end of the training, participants gain a more mature engineering perspective that enables them to match enterprise problems to the right agent-solution patterns, design planning and orchestration logic according to production needs, build more reliable tool-calling layers, choose memory strategies by use case, make quality sustainable through evaluation and observability, reflect security and governance requirements into technical solutions, and move agent-based AI projects from prototype to production. ## Kazanımlar - 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.

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.

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