Which framework do you use for AI agents?

Depends. LangGraph for complex planning, OpenAI Assistants for tool-use, or hand-rolled Python when the agent's logic doesn't fit a framework.

How do you handle agent hallucinations in production?

Strict function-call schemas, output validation, tool-call retries, and human-review gates on consequential actions (money, communications, deletions).

AI Agents Development

AI agents are the next generation beyond chatbots — LLMs given goals and tools, able to take multi-step actions autonomously. Book meetings, research prospects, draft emails, execute API calls, negotiate delivery slots. We build agents that reliably ship business outcomes, not demos.

By WebsiteAndGoUpdated Aug 4, 2026 4 min read

Why this matters

Multi-tool agents (calendar, CRM, email, search, custom APIs)
Goal-based planning with LangGraph or OpenAI's assistants
Human-in-the-loop for high-stakes actions
Memory across sessions
Full observability of agent decisions

Where AI agents ship real value

Best fit: repetitive multi-step tasks — outbound lead research, meeting prep, competitor monitoring, internal helpdesk triage, or complex customer support flows. The pattern: several tools + clear goal + human review of consequential actions.

Why most AI agent demos fail in production

Insufficient tool descriptions, no memory management, no error recovery, and no observability. Our agents ship with all four handled from Day 1 — that's the difference between demo and production.

What's included

Multi-tool LangGraph Memory Human-in-loop Observability

Frequently asked questions

Depends. LangGraph for complex planning, OpenAI Assistants for tool-use, or hand-rolled Python when the agent's logic doesn't fit a framework.
Strict function-call schemas, output validation, tool-call retries, and human-review gates on consequential actions (money, communications, deletions).

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