Agents that finish the task.
Tool use, planning, memory and guardrails composed into agents that complete real work — drafting, researching, triaging, scheduling, transacting. With observability and human-in-the-loop where the stakes demand it.
The problem we solve
Agentic systems are powerful and easy to get badly wrong. Loops that never terminate. Tool calls that hallucinate parameters. Memory that leaks across users. Costs that spiral on a single bad input. We build agents with the discipline of distributed systems engineering — finite state, bounded resources, observable steps, traceable decisions, fail-safe defaults.
What we ship
- 01Agent architecture: state machines, planners, tool routers
- 02Tool calling with strict schemas and validation
- 03Memory: short-term, long-term, per-user, per-task
- 04Guardrails: cost caps, time-outs, iteration limits, content filters
- 05Human-in-the-loop checkpoints for high-stakes steps
- 06Multi-agent orchestration with clear ownership
- 07Tracing every step for debug and audit
- 08Replay and time-travel for incident investigation
- 09Sandboxed execution where agents touch real systems
- 10Eval harnesses for agent task completion, not just answers
What you receive
- Production agent integrated with your tools and APIs
- Trace and replay tooling your team can use for debug
- Eval suite for end-to-end task completion
- Operational guardrails written down and tested
Stack we reach for
Ideal for
- → Workflows with clear inputs, outputs and tools — but variable paths
- → Internal operations: data entry, triage, research, monitoring
- → Customer support augmentation beyond simple chat
- → Developer tools: code review, test generation, refactoring assistants
How an engagement runs
- 01
Task analysis
We decompose the target task into states, tools, decisions. We are honest about whether it's actually an agent or a simpler pipeline.
- 02
Prototype
End-to-end agent on a narrow task with all tools wired up. Tracing on from the first run.
- 03
Guardrails & evals
Cost, time and iteration limits, content guardrails, eval harness against representative tasks.
- 04
Production
Rollout with human-in-the-loop on high-stakes steps, full observability, runbook.
How to engage
Agent Feasibility
Task analysis and prototype demonstrating whether the workflow is agent-suitable.
Agent Build
End-to-end agent shipped with guardrails, evals and operational maturity.
Agent Operate
Continuous improvement as the agent encounters real-world cases.
Frequently asked.
01When does a workflow really need an agent?
When the path through the work genuinely varies based on intermediate results. If the path is fixed, a deterministic pipeline with LLM steps is simpler, cheaper and safer. We will tell you when you don't need an agent.
02How do you handle agents that go off the rails?
Iteration limits, cost caps, content guardrails, human checkpoints. We sandbox tool calls that touch real systems and we trace every step so we can see what happened.
Have a problem worth solving well?
Tell us the outcome you want. We'll tell you what it takes — honestly, within a week, in writing.
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