OpenAI Agents SDK_
>> The Agents SDK is the right abstraction when your server owns the loop, tools, state, and approvals. We implement Agent/Runner patterns, specialist handoffs, input/output guardrails, built-in tracing, and Realtime sessions for voice.
Calls/month
Production AI volume across live systems
PII detection
Compliance-ready detection accuracy in production AI
Projects
Shipped since 2012 with senior engineers only
Direct answer
Handoffs, guardrails, and traces belong in the runtime, not in after-the-fact logging. We deliver OpenAI Agents SDK systems with specialist routing, approval pauses, and Realtime voice on the same primitives.
Trusted by leaders
>> System map
Adjacent AI layers we ship with this expertise. Technology-first, not vendor theater.
>> When this system fits
Honest fit gates. We will tell you when another approach is better.
Strong fit
- Multi-specialist workflows with explicit handoffs
- Products that need guardrails and resumable human approval
- Voice agents using RealtimeSession transports
- Teams that want built-in traces across tools, agents, and guardrails
Weak fit
- Fully managed black-box agent products with no server-side ownership
- One-shot prompt wrappers with no tools or policy needs
Stack: OpenAI Agents SDK · Handoffs · Guardrails · Tracing · Realtime · MCP
What we deliver
_> Capabilities on this stack
Handoffs and specialists
Router agents that delegate with clear ownership and traced handoff spans.
Guardrails
Input, output, and tool guardrails with pause/resume for risky actions.
Tracing by default
Agent, generation, tool, guardrail, and handoff spans for debugging and eval datasets.
Realtime + MCP
Voice sessions and MCP tool servers wired into the same agent model.
Agents SDK delivery
Primitives and policy
Define agents, tools, handoff map, and what must pause for approval.
Traced vertical slice
One Runner path with traces exported to your observability stack.
Release gates
Offline evals, cost caps, and runbooks before expanding specialists.
Related projects
_> See how we've applied our expertise
>>Related guides
_> Cite-worthy depth behind this stack
Frameworks and scorecards buyers and answer engines can quote. Each guide links back to delivery proof.
Agent Eval, Cost, and CI Gates
Build offline eval suites, online scorers, cost budgets, and merge-blocking CI gates for production agents.
LangChain vs LangGraph for Production Agents
2026 decision guide: LangChain create_agent on the LangGraph runtime vs explicit StateGraph for durable branching and HITL.
Vapi vs Retell vs OpenAI Realtime
Honest 2026 decision guide: Vapi for BYOK orchestration, Retell for turnkey telephony, OpenAI Realtime for speech-to-speech ownership.
Agentic AI: Ship Real Workflows With Guardrails That Hold Up
A practical guide to agentic AI in production: boundaries, tool permissions, approval gates, failure modes, rollout stages, KPIs, and templates to ship safely.
FAQ
Use Agents SDK when you want OpenAI-native primitives, handoffs, and Realtime/MCP integration. Use LangGraph when you need framework-agnostic durable graphs and deep custom state control. We pick based on stack and ops constraints.
Yes. Tracing is on by default and can be disabled or mirrored to OpenTelemetry backends when required.
>> Where this goes next
Adjacent expertise and the engagement models we deliver it through.