Senior Engineers, Not Layers of Mediation
Direct access to engineers who can inspect, decide, and execute.
What We Solve
Turn agentic ideas into systems a serious company can trust.
More products will include agents. The winners will not be the teams with the most demos. They will be the teams whose agents stay useful, bounded, observable, and economically rational.
We work on the hard parts: tool reliability, permission boundaries, evaluation coverage, rollout safety, memory design, model routing, and the cost of letting autonomous workflows run at scale.
- Prompt-only prototypes that collapse in real workflows
- Unreliable tool calls and broken action chains
- No evaluation harness for regressions, drift, or unsafe behavior
- Weak approvals for actions with business or security impact
- Agent sprawl without architecture or ownership
- Rising model spend from inefficient routing and retries
- Low observability across prompts, tools, and outcomes
- Vendor lock-in that makes iteration slower and more expensive
Agentic AI becomes valuable only when it becomes controllable.
What You Get
- Agent architecture with clear tool, model, and state boundaries
- Evaluation framework for correctness, safety, and business usefulness
- Guardrails and approvals for high-risk actions and sensitive data paths
- Observability layer across prompts, tools, latency, and outcomes
- Rollout plan for staged launch, monitoring, and iteration
Coverage and Delivery
Agent Architecture
- Single-agent and multi-agent workflows
- Tool gateways, state handling, and workflow boundaries
- Retrieval, memory, and context-shaping strategies
- Model routing and fallback logic
Trust and Safety
- Approval flows for sensitive or irreversible actions
- Guardrails around tools, data, and output channels
- Evaluation datasets and scenario-based testing
- Logging and incident-ready observability
Typical Outputs
- Architecture map and orchestration plan
- Evaluation and rollout framework
- Cost and latency control recommendations
- Roadmap for production hardening
Business Fit
- Products moving from AI feature to AI workflow engine
- Internal automation with permissioned actions
- Support, ops, and knowledge systems needing real reliability
- Leadership teams that want agentic AI without operational chaos
Why Teams Choose SToFU When Stakes Are High
Senior engineering. Clear decisions. Real outcomes.
Commercially Useful Outputs
Scope, priorities, remediation, and next steps your team can use immediately.
Built for AI-Era and High-Stakes Systems
AI-native platforms, native software, secure systems, and low-latency infrastructure.
Share the system, the pressure, and the deadline. We will turn that into a concrete next move.