Technology
Agentic AI Workflow Development — Tool-Using Agents That Ship
Multi-step AI agents that plan, call tools, write to systems, and stay inside policy — with human-in-the-loop checkpoints where it matters.
What we build with Agentic Workflows
- Planning and tool-using agents with OpenAI, Claude, or open models
- Workflow orchestration with LangGraph, OpenAI Agents SDK, or custom
- Memory layers: short-term state, long-term vector recall, and audit logs
- Human-in-the-loop checkpoints for irreversible or sensitive actions
- Safe execution sandboxes for code, browser, and API tools
- Policy guardrails, allowlists, and per-action authorization
Why DiveScale
Built by engineers who ship Agentic Workflows in production
Agentic systems can produce serious leverage — or serious incidents. DiveScale ships agents that earn their autonomy: small action surfaces first, strong guardrails, clear audit trails, and human checkpoints for anything that writes to the world.
We do not chase agent-of-everything demos. We scope the workflow, decide which steps the agent owns vs. proposes, and build the observability that lets your operations team trust the system.
Architecturally we work across LangGraph, the OpenAI Agents SDK, and bespoke orchestrators, choosing per workload — and we benchmark against simpler prompt-based solutions before going agentic.
Agentic Workflows use cases we deliver
How we deliver
Our Agentic Workflows delivery process
- 01
Scope the workflow
We map the human workflow, identify what the agent should own, propose, or never touch — and define escalation paths.
- 02
Design the tool surface
Each tool is typed, allowlisted, and rate-limited. Irreversible actions require human approval.
- 03
Build with evals & traces
We build with Langfuse or LangSmith tracing from day one so every agent decision is reviewable.
- 04
Ship with guardrails
Production rollout starts in shadow mode, progresses to suggest-only, and finally to act-with-checkpoint based on measured reliability.
Related technologies
LLMs
Production LLM engineering — model selection, RAG, fine-tuning, evals, guardrails, and the operational layer that keeps quality high.
Learn moreOpenAI
Production-grade integrations with GPT-4o, GPT-4.1, o-series reasoning models, Realtime voice, embeddings, and the Assistants API.
Learn moreAnthropic (Claude)
Production builds on Claude Opus, Sonnet, and Haiku — long-context reasoning, tool use, prompt caching, and Computer Use agents.
Learn morePython
Production Python engineering — FastAPI services, async pipelines, AI/ML workloads, data engineering at scale, and the typed, tested, observable discipline production Python deserves.
Learn moreAgentic Workflows — Frequently Asked Questions
We pick per workload. LangGraph for complex stateful flows; OpenAI Agents SDK for OpenAI-centric builds; custom when neither fits. The orchestration layer is an implementation detail — workflow design is the hard part.
