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% →
The Context Engineering Era: Prompt Caching, Long Context vs RAG, and Runtime State Management (2026 Guide)
Prompt engineering is dead, context engineering is alive. Anthropic's 90% cost-cutting prompt caching, GPT-5.5's 272K input threshold, Claude Opus 4.7's 1M context, and agent runtime state management are rewriting AI engineering in 2026. Turkish token efficiency, KVKK-compliant state stores, the 'Don't Break the Cache' principle.
The Context Engineering Era: Prompt Caching, Long Context vs RAG, and Runtime State Management (2026 Guide) →
Claude Opus 4.7 vs GPT-5: Which is Better? — A 2026 Flagship Model Head-to-Head Comparison
A head-to-head comparison of the two 2026 flagship AI models — Anthropic Claude Opus 4.7 and OpenAI GPT-5. Architecture and training philosophy differences (Constitutional AI vs RLHF), benchmark results (MMLU, HumanEval, GSM8K, hallucination), Turkish performance, code generation, reasoning, long context (1M vs 256K), multimodal, agent/tool use/MCP, cost, latency, safety, and alignment. Use-case-based winner analysis.
Claude Opus 4.7 vs GPT-5: Which is Better? — A 2026 Flagship Model Head-to-Head Comparison →
Replace Classic RAG with Agentic RAG in 2026: Production Architecture on LangGraph
Naive RAG's six fatal weaknesses are fully solved in 2026 by agentic RAG. A production-grade RAG with plan/reflect/verify loops, hybrid retrieval, and claim-verification built on the LangGraph v0.4 state-graph used by Klarna, LinkedIn, and Uber — plus a KVKK-compliant Turkish bank case study and cost-latency tradeoffs.
Replace Classic RAG with Agentic RAG in 2026: Production Architecture on LangGraph →
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 →
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 Definitions Caching: 50 Tool'lu Agent'ta Strateji
Modern agent'larda 20-50 tool var. Her tool definition ortalama 200-500 token. Toplam 10-25K token sadece tool listesi. Bu derste tool cache stratejilerini, dinamik tool seçimini ve sıralama trick'lerini öğreneceksin.
Tool Definitions Caching: 50 Tool'lu Agent'ta Strateji →
Structured Output Pitfalls: JSON Mode Token Greed and the Real Cost of Tool-Use Forcing
Using JSON mode doesn't mean 'fewer tokens' — in most cases it uses **more tokens**. Schema complexity, field name length, escape characters — all hidden token costs. This lesson covers cost-aware structured output design.
Structured Output Pitfalls: JSON Mode Token Greed and the Real Cost of Tool-Use Forcing →
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: 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 →
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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