AI Agent Development: Assistants That Do Real Work

A chatbot answers questions. An agent does work: it checks your systems, takes actions, follows up, and reports back. That difference is why agent development is a separate discipline — and why Sceniox treats it as one.

Beyond chatbots

Agents come in several shapes. Single-agent task runners and autonomous assistants handle defined jobs end to end. Multi-agent orchestration splits bigger jobs across specialists — hierarchical, sequential, swarm or debate setups. ReAct, plan-and-execute and Reflexion patterns let agents reason, act, observe the result and correct course. And human-in-the-loop approval workflows keep a person in charge of anything irreversible.

Architecture that holds up

Real agents need memory and tools. Short-term conversational memory plus long-term episodic, semantic and procedural memory, backed by vector databases like Pinecone, Qdrant, Weaviate, pgvector or Milvus, with agentic RAG and GraphRAG retrieval. Tool use through function calling and external APIs, Model Context Protocol (MCP) servers and clients, agent-to-agent communication, headless browser automation, and sandboxed code execution for anything untrusted.

Frameworks, models and AgentOps

Sceniox builds on LangGraph and LangChain, CrewAI and AutoGen, the OpenAI and Claude Agent SDKs and Google ADK, plus LlamaIndex, Pydantic AI and Mastra. Models run local (Ollama, vLLM, LM Studio) or through cloud routing gateways (OpenRouter, LiteLLM, Bedrock, Vertex AI), with fine-tuning via LoRA and distilled small models where cost matters. And AgentOps keeps it safe in production: execution tracing with LangSmith or Langfuse, evaluation and hallucination testing, permission scoping, rate limits and cost circuit breakers.