Agentic AI Development
We build production AI agents: tool use, multi-agent orchestration, state management, evals, observability. Agents that actually finish the task.
Why we picked this stack
Agents are how LLM applications evolve beyond simple chat. Most agent products don't fail because of model quality; they fail because of state management. We've shipped agentic systems with real observability, so we know what breaks in production. We treat agents like distributed systems: idempotency, retries, partial failure, and audit trails are designed in from day one, not bolted on after the demo.
What we build with it
Single-agent systems with tool use
Function calling and structured outputs, with scoped tools and reliable exit criteria.
Multi-agent orchestration
Planner / worker / critic patterns coordinated with LangGraph or custom orchestration.
Agent state management
Memory, context-window management, and checkpointing for resumable, long-running tasks.
Tool integration layers
Custom tool definitions, input validation, and error handling around every external call.
Agent observability
Langfuse, Helicone, and custom logging: full traces of what the agent did and why.
Eval harnesses for agent behavior
Regression tests on critical paths and A/B testing of new agent versions against control.
The technology is the starting point, not the pitch
Versions and libraries we use
- OpenAI Assistants APIFunction calling, structured outputs, and the Assistants tool loop.
- Anthropic ClaudeTool use and computer use for agents that operate real interfaces.
- LangGraphOur default for stateful, multi-agent flows: explicit graphs over implicit chains.
- CrewAI, AutoGenUsed selectively: we lean toward LangGraph or custom orchestration for production.
- Pydantic AIEmerging: type-safe agents with validated inputs and outputs.
- LangfuseAgent observability: traces, evals, and production logging.
- Custom orchestration over LangChainProduction-grade control flow when off-the-shelf agent frameworks fall short.
Anti-patterns we avoid
Agents with no error recovery
Production agents fail mid-task. Plan for retries, partial failure, and graceful exits.
Skipping evals
Agent behavior drifts as prompts and models change. You need regression tests.
Unscoped tool access
Letting an agent call any tool is a security and cost risk. Scope permissions per agent.
Token-blind multi-agent systems
Cost spirals fast when agents talk to agents. Budget tokens per run and route models.
Agents where a function call works
Don't replace deterministic code with an agent. A plain function is cheaper and reliable.
Where agentic AI ships most
AI Marketing
Research agents, outbound automation, and content + enrichment workflows.
- Research + enrichment agents
- Outbound automation
- Content workflow agents
AI Fintech
Compliance and fraud-investigation agents over risk and document pipelines.
- Compliance investigation agents
- Fraud investigation
- Document-intelligence agents
AI Healthtech
Clinical workflow agents for triage, intake, and document handling.
- Clinical workflow agents
- Triage + intake
- Medical document agents
Agentic products we’ve shipped
AI Ads Assistant for Meta and TikTok
We built a minimum viable product to connect disparate advertising APIs into a single, intuitive user interface.
AI Contract Generation Platform
We built the technical foundation for a SaaS platform that automates complex legal document creation with usage-based pricing system
AI Mental Health Companion
We engineered a voice-first AI companion with a therapeutic framework to validate a new model for mental health support.
Hire senior agent engineers
Need to extend your team with engineers who have shipped production agents? We embed senior AI engineers who treat agents like distributed systems: state-aware, eval-driven, and cost-aware from day one.
Frequently asked questions
Function calling is single-shot. Agents loop until the task is complete: they need state, error handling, and explicit exit criteria.
Production engineering
The technology is the starting point, not the deliverable
We select tools for the problem, not for the portfolio. Every technology detail page on this site describes a stack we reach for because it ships, not because it trends.
Building an agentic product? Schedule a meeting.
No account managers. No discovery theatre. A direct conversation about your agent architecture and what it takes to ship it to production.