AI Agent Development

An AI agent plans several steps towards a goal, uses external tools (APIs, databases, files) and keeps its state across those steps. A chatbot works in a single question-and-answer loop; it does not use tools or keep state. Agents can run autonomously inside live workflows under human oversight; chatbots stay limited to exchanging information.

RAG is the right choice for content that changes often: internal documentation, product knowledge bases, regulation. Fine-tuning is for lasting changes in style or behaviour: domain tone, format rules, terminology. For most B2B use cases RAG is enough and carries less operational risk: a wrong answer is fixed by updating the source, while fine-tuning needs retraining. The two can also be combined: a fine-tuned model with a RAG knowledge layer on top.

It depends on the context. LangGraph suits state-machine based, predictable flows. AutoGen is strong in group chat and debate patterns. CrewAI gives a quick role-based start but stays limited at scale. For complex production systems we prefer custom orchestrators written on top of the model providers' SDKs; they give better observability and cost control.

There are three layers: STT that turns speech into text (for example Whisper, Deepgram), a language model that reasons (Claude, GPT, Gemini, depending on the task and latency needs) and TTS that turns text into speech (for example ElevenLabs, Cartesia). Low latency needs streaming at every layer. In live use, interruptions by the user, calls to external systems (function calling) and voice activity detection (VAD) are handled separately.

The main levers are an evaluation (eval) setup, prompt caching, model routing that sends each task to a smaller or larger model by difficulty, avoiding repeated tool calls and a budget limit on the agent loop. First we take a baseline for token spend, loop count and tool calls; the effect of each improvement is verified with a before / after comparison on the same metrics.

In three layers: (1) Risk classification: determine whether the system is limited-risk, high-risk or prohibited under the EU AI Act. (2) Data governance: for KVKK (Turkish data protection law), data minimization, cross-border transfer rules and the right to object to an adverse outcome produced solely by automated processing (KVKK Art. 11). (3) Operational controls: audit logging, model cards, human oversight points and an incident response plan. High-risk systems add bias and robustness testing. GG Tech Teknoloji holds ISO/IEC 42001 certification for its AI management system.