Tool-using agents
Scoped access to search, databases, internal APIs and external services with typed inputs, validated outputs and auditable actions.
Agentic workflows that can be controlled, resumed and improved
AI Agents
AI AGENTS & ORCHESTRATION
Tool-using agents, stateful workflows and human approval flows built around explicit state, permissions, recovery and measurable outcomes.
We build systems in which models select or use tools across multiple steps, while deterministic application logic controls permissions, branching, persistence, retries and human decisions. LangChain or LangGraph may support this architecture when their abstractions fit the workflow.
Long-running AI workflows with explicit state and control.
Long-running AI workflows with explicit state and control.
We build systems in which models select or use tools across multiple steps, while deterministic application logic controls permissions, branching, persistence, retries and human decisions. LangChain or LangGraph may support this architecture when their abstractions fit the workflow.
Stateful orchestration
Explicit workflow state, branching, checkpoints, durable execution and recovery for work that cannot live inside one request.
Human approval
Review queues, thresholds, escalation and editable intermediate results for actions that require accountability.
Multi-agent systems
Specialized agents only where decomposition improves quality or ownership enough to justify coordination cost and complexity.
How we make an agentic workflow production-safe
How we make an agentic workflow production-safe
We separate decisions that benefit from a model from rules that should remain deterministic. Every external action has an owner, permission boundary, validation path and recovery behavior.
- 01
Map the workflow graph
Identify states, tools, decisions, failure points, approval gates, completion criteria and existing system owners.
- 02
Evaluate the uncertain steps
Build datasets for routing, extraction or generation and compare model behavior before automating actions.
- 03
Add durable execution
Implement persistence, queues, idempotency, retries, timeouts, cancellation and human review.
- 04
Trace and govern
Record model, prompt, tool and state transitions, monitor cost and failures and support controlled configuration rollout.
Agent and orchestration deliverables
Agent and orchestration deliverables
The system includes the backend mechanics required for reliable work, not only the agent prompt and tool declarations.
- Workflow and responsibility graph
- Agent, tool and deterministic control implementation
- Persistent state, queues and recovery
- Human review and administrative controls
- Tracing, evaluation and operating documentation
Agent and orchestration deliverables
Long-running AI workflows with explicit state and control.
Long-running AI workflows with explicit state and control.We build systems in which models select or use tools across multiple steps, while deterministic application logic controls permissions, branching, persistence, retries and human decisions. LangChain or LangGraph may support this architecture when their abstractions fit the workflow.
FAQ
Questions specific to this path.
Do you always use LangChain or LangGraph?
No. We use them when their model, tool or graph abstractions reduce risk and maintenance. A smaller explicit implementation is preferable for simpler workflows.
When does a multi-agent design make sense?
When distinct contexts, tools or review responsibilities materially improve the workflow. Extra agents are not a substitute for clear state and control.
Can an agent perform actions in our systems?
Yes, with scoped authorization, typed tool contracts, validation, audit logs and approval gates proportionate to the action's consequence.
NEXT STEP
Bring us the constraint, not a polished brief.
A technical lead will review the current state and recommend the smallest useful next step.
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