The AI conversation in 2025 has shifted decisively from "what can models say?" to "what can models do?" The agent paradigm — AI systems that take actions, use tools, and complete multi-step tasks — is no longer experimental. It is becoming the default way to build with AI.
What Is an AI Agent?
An AI agent is an LLM with the ability to:
- Observe — receive input from the environment (user messages, tool outputs, file contents)
- Reason — decide what to do next based on its observations
- Act — execute tools (run code, browse the web, call APIs, write files)
- Iterate — observe the results and repeat until the task is complete
The key difference from a chatbot: an agent does not just respond to messages. It works toward a goal across multiple steps, adapting its approach based on what it learns along the way.
The Tool Use Revolution
The foundation of the agent paradigm is tool use. Every major model provider now supports it:
- Function calling — models can output structured JSON to invoke predefined functions
- Computer use — Anthropic's Claude can control a desktop, clicking buttons and typing in applications
- Code execution — models can write and run code, inspect outputs, and iterate
- Web browsing — models can search the web, read pages, and extract information
This turns LLMs from text generators into general-purpose problem solvers.
Agent Frameworks
The ecosystem has matured rapidly. Key frameworks include:
OpenAI Agents SDK — OpenAI's official framework for building multi-agent systems with handoffs, guardrails, and tracing.
Anthropic Claude Code — a CLI agent that can navigate codebases, write code, run tests, and commit changes autonomously.
LangGraph — a framework for building stateful, multi-step agent workflows with complex branching and human-in-the-loop patterns.
CrewAI and AutoGen — multi-agent frameworks where specialized agents collaborate on complex tasks.
Real-World Agent Applications
Agents are already in production across several domains:
Software engineering — AI agents that can take a GitHub issue, read the codebase, write a fix, run tests, and submit a pull request. Claude Code and similar tools are being used daily by engineering teams.
Customer support — agents that can look up order status, process refunds, update accounts, and escalate to humans only when necessary.
Research — agents that can search academic papers, synthesize findings, and produce literature reviews across hundreds of sources.
Data analysis — agents that can write SQL queries, generate visualizations, interpret results, and iterate on their analysis based on follow-up questions.
The Challenges
Agent systems are powerful but come with real challenges:
Reliability — agents can go off track, especially on ambiguous tasks. A coding agent might "fix" a test by deleting it rather than fixing the underlying code.
Cost — multi-step agent workflows can involve dozens of LLM calls. A single complex task might cost $1-10 in API calls.
Evaluation — how do you measure whether an agent "did a good job"? Traditional benchmarks do not capture the full picture.
Security — agents that can execute code, browse the web, and access APIs create a larger attack surface. Prompt injection becomes a real security concern.
What Is Coming
The agent paradigm is still in its early innings. We expect to see:
- Longer-running agents that work on tasks over hours or days, not just seconds
- Multi-agent collaboration becoming more sophisticated
- Better reliability through improved planning, verification, and self-correction
- Standardized protocols for agent-to-agent and agent-to-tool communication
For builders in the Turkish AI community, now is the time to start experimenting with agent architectures. The tools are available, the patterns are emerging, and the applications are everywhere. The question is no longer whether AI agents will transform how software works — it is how fast.