Agentic AI and Autonomous Systems
Agentic AI is the architecture in which a large language model — instead of producing a single answer — autonomously completes multi-step tasks by combining planning, tool use, memory and feedback loops.
- Agentic AI and Autonomous Systems
- Agentic AI is the architecture in which a large language model — instead of producing a single answer — autonomously completes multi-step tasks by combining planning, tool use, memory and feedback loops.
What you will learn in this pillar
- 01ReAct loop and the plan-act-observe pattern
- 02Typed-state agent design with LangGraph
- 03Role-based multi-agent composition with CrewAI
- 04Tool design and schema engineering
- 05Memory layers (short-term / long-term / episodic)
- 06Guardrails: budgets, retries and prompt-injection defense
In-depth Explanation
Blog posts on this pillar
What is an AI Agent? Autonomous AI Architectures in 2026 — A Comprehensive End-to-End Guide
A comprehensive 2026 reference explaining how AI agents work, which architectures solve which problems, and what they mean for Turkish enterprises. Covers ReAct, multi-agent, MCP, tool use, computer use, browser agents, frameworks (LangGraph / AutoGen / CrewAI / Claude Code), production concerns, evaluation, security, KVKK compliance, and three anonymized Turkish case studies.
What is an AI Agent? Autonomous AI Architectures in 2026 — A Comprehensive End-to-End Guide →
Anthropic's Multi-Agent Architecture: How the Orchestrator-Worker Pattern Beats Single-Agent by 90.2%
Anthropic's Multi-Agent Research system beat single-agent Claude Opus by 90.2% on internal research evals using an orchestrator-worker pattern. This guide covers lead agent + parallel subagent architecture, structured artifact handoffs, planner-generator-evaluator loops, Claude Agent SDK with .claude/agents/, cost caps, deadlock prevention, comparisons with CrewAI/LangGraph/AutoGen, and a Turkish law-firm contract-analysis case.
Anthropic's Multi-Agent Architecture: How the Orchestrator-Worker Pattern Beats Single-Agent by 90.2% →
What Is a Multi-Agent System?
What is a multi-agent system? A multi-agent system is an architecture where several AI agents, each with its own role, solve a task together by dividing the work and communicating with one another. This guide: a clear definition, the difference from a single agent, how it works, agent orchestration, task division, LangGraph and CrewAI, real-world examples, limits, and FAQs.
What Is a Multi-Agent System? →
Single-Agent or Multi-Agent? How to Choose the Right Agent Architecture for the Right Problem
As AI agent systems become more common, one of the most important architectural questions is whether to use a single powerful agent or distribute tasks across multiple specialized agents. Many teams assume multi-agent systems are automatically more advanced, leading to unnecessary complexity. Others force truly separable workflows into a single agent and lose quality, control, and scalability. This guide compares single-agent and multi-agent architectures across technical, operational, cost, security, observability, coordination, and governance dimensions, and explains how to choose the right architecture for the right enterprise problem.
Single-Agent or Multi-Agent? How to Choose the Right Agent Architecture for the Right Problem →
What is MCP (Model Context Protocol) and Why Did It Become the 'USB-C of AI' Standard in 2026? — Mapping the 5,000+ Server Ecosystem
Launched by Anthropic in November 2024, the Model Context Protocol (MCP) became the 'USB-C of AI' in 2026 — with 5,000+ servers, adoption by OpenAI, Google, Microsoft, and Salesforce, and its own Wikipedia page. This guide covers MCP's three primitives, transport layer, OAuth 2.1, building your own server, security risks, and Turkish-market opportunities for Yargıtay/BIST/KVKK-style MCPs.
What is MCP (Model Context Protocol) and Why Did It Become the 'USB-C of AI' Standard in 2026? — Mapping the 5,000+ Server Ecosystem →
Prompt Engineering: From Zero to Advanced — A Comprehensive 2026 Guide
A comprehensive Turkish guide that takes prompt engineering from zero to advanced. Covers the 6 components of a prompt, 14 core techniques (zero-shot, few-shot, CoT, ToT, ReAct, self-consistency, meta-prompting), Turkish-specific notes, 20+ ready templates, model-specific differences (GPT-5, Claude Opus 4.7, Gemini 3), prompt injection defenses, DSPy-based automatic optimization, and A/B testing.
Prompt Engineering: From Zero to Advanced — A Comprehensive 2026 Guide →
Learning content
ReAct: Reasoning + Acting Bütünleşmesi
Düşünme + tool kullanma karışımı. Modelin Thought → Action → Observation döngüsüyle problem çözmesi. Agent'ların temel pattern'i.
ReAct: Reasoning + Acting Bütünleşmesi →
ReAct Pattern: Reasoning + Acting Döngüsü
Modeli düşünme + harekete geçme döngüsünde tutmak. Web araması, hesaplama, API çağrılarıyla zincirleme akıl yürütmenin temeli.
ReAct Pattern: Reasoning + Acting Döngüsü →
Tool Use: Granting Claude Real Capabilities
How to teach Claude to use a calculator, database, email, Slack, code sandbox? Anatomy of tool use and production patterns.
Tool Use: Granting Claude Real Capabilities →
Tool Use + Function Calling: LLM's Doors to External World — From OpenAI Tools to MCP
Tool Use anatomy: LLM reading JSON schema tool definitions, choosing right tool with right parameters. OpenAI function calling (June 2023), Anthropic MCP (Model Context Protocol, Nov 2024), Llama-3 tool tokens. Production agent patterns: ReAct, Plan-and-Execute, Reflexion. Turkish agent practice.
Tool Use + Function Calling: LLM's Doors to External World — From OpenAI Tools to MCP →
Tool Use History: From Yao 2022 ReAct to Anthropic MCP — 3-Year Birth of LLM Agents
Historical and conceptual anatomy of LLM agents: Yao et al. 2022 ReAct paper ('Reasoning + Action' fusion), OpenAI function calling (June 2023, first standardization), Anthropic MCP (November 2024, open standard). Rise of LangChain, AutoGen, CrewAI frameworks. 'Why aren't LLMs sufficient alone, why do they need tools?' Practical face of AGI debate. Turkish agent use cases.
Tool Use History: From Yao 2022 ReAct to Anthropic MCP — 3-Year Birth of LLM Agents →
Tool Use API: Function Calling in Practice
Finish tool use over the API: full loop, parallel tools, error feedback, and schema validation.
Tool Use API: Function Calling in Practice →
Related training
Building Production AI Applications with LangChain and LangGraph Training
A 3-day advanced training for senior developers and AI engineers who want to build production-grade multi-agent AI applications with the LangChain, LangGraph, LangSmith, and LangServe ecosystem in a provider-agnostic architectural approach. Includes LCEL, RAG patterns, stateful agents, evaluation, and deployment.
Building Production AI Applications with LangChain and LangGraph Training →
Multi-Agent System Design with CrewAI + Python Training
A 3-day advanced CrewAI training for Python developers and AI engineers who want to rapidly build production-grade agent systems with the role-based multi-agent paradigm. Includes Sequential & Hierarchical processes, Tools, Memory, CrewAI Flows, and Enterprise deployment.
Multi-Agent System Design with CrewAI + Python Training →
Frequently Asked Questions
LangGraph or CrewAI — which should I pick?▾
If you need complex state, branching flows and human-in-the-loop checkpoints — LangGraph. If you want a quick role-based 'AI team' (planner / writer / critic) — CrewAI. LangGraph is the production tool; CrewAI is the rapid-prototyping tool.
How many tools should an agent have?▾
A practical ceiling is 7–10 tools per agent. Beyond that, tool-selection accuracy degrades sharply. If you need more, split with a hierarchical (master + sub-agent) pattern.
How are agents protected against prompt injection?▾
Defense-in-depth: (1) input sanitization and system-prompt sealing; (2) approval gates on high-risk tool calls; (3) sanitization of tool outputs to strip injected instructions. No single layer is sufficient alone.
How are agent costs controlled?▾
Per-agent token budgets, tool-call caps, tiered models (strong for planning, cheap for execution) and caching (Anthropic prompt caching, semantic cache). Every run gets a hard timeout and a circuit breaker.
Is multi-agent really necessary?▾
For most use-cases — no. A single agent with a good tool set is usually cheaper and more reliable than 'orchestrator + 5 workers'. Multi-agent earns its complexity only with genuinely parallel work or distinct specialties.
Where should human-in-the-loop checkpoints sit?▾
Before any irreversible action (sending email, payment, deletion) and whenever confidence drops below a threshold. In LangGraph these are modeled as 'interrupt' nodes.
Other pillar topics
Enterprise AI Consulting
Enterprise AI consulting is the end-to-end discipline that takes AI from business objectives to technical architecture, prioritizing use-cases and shaping a production-ready roadmap so AI scales sustainably inside the organization.
RAG (Retrieval-Augmented Generation) Architecture
RAG (Retrieval-Augmented Generation) is an architecture that grounds large-language-model answers in chunks retrieved from the organization's own documents or data sources, providing both freshness and citations.
LLMOps: Production-Grade LLM Operations
LLMOps is the engineering discipline that covers the development, deployment, monitoring, evaluation and cost management of LLM-powered applications — extending classic MLOps with prompt versioning, eval-driven CI and observability tailored for non-deterministic systems.
AI Governance and EU AI Act Compliance
AI Governance is the corporate framework that ensures AI systems — from design to use — meet ethical, safety, transparency, explainability and legal-compliance requirements (EU AI Act, GDPR/KVKK, ISO 42001).
Corporate AI Training
Corporate AI training is a structured program — calibrated to different role levels from executives to engineers — that builds AI capability through hands-on, scenario-grounded learning with measurable outcomes.
Industry AI Use Cases
AI use cases are a pragmatic decision guide — across banking, healthcare, retail, public sector and beyond — capturing the concrete business value, success metrics and reference architectures that make AI worth building.
Prompt and Context Engineering
Prompt engineering is the applied discipline of designing instructions, examples, context and output controls so that an LLM produces consistent, accurate and cost-efficient outputs.
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