Agentic AI
Software that decides, not just predicts
Agents earn their place when they close a loop a person used to close by hand. We build them with explicit tools, hard boundaries and a full trace of every step — so you can audit what happened and why.
Capabilities
What this actually includes
The concrete pieces of work, so you can tell what you are buying rather than inferring it.
Tool and action design
Every capability an agent has is a typed, permissioned function with its own tests. No open-ended shell access, no surprise side effects.
Planning and control flow
Deterministic orchestration around a non-deterministic core, so retries, branches and stop conditions behave predictably under load.
Human-in-the-loop gates
Approval checkpoints on anything that spends money, writes to a system of record or contacts a customer.
Evaluation harnesses
Task-level scoring against a fixed suite, run in CI, so a prompt or model change cannot quietly regress behaviour.
Tracing and replay
Full step traces with inputs, tool calls and costs, replayable against a past state when you need to explain a decision.
Cost and rate governance
Per-tenant budgets, token ceilings and circuit breakers that fail closed rather than draining an account.
How we work
The sequence we follow
Find the closable loop
We map the manual workflow end to end and identify where an agent genuinely removes handoffs — and where a plain script would do the job cheaper.
Build the eval set first
Before any agent code, we assemble scored task cases from real historical work. That set becomes the definition of done.
Ship a narrow agent
One workflow, tight tool surface, aggressive guardrails, shadow mode against live traffic until the scores hold.
Widen under supervision
Expand scope one tool at a time, watching the eval suite and the cost curve as autonomy increases.
Operate and tune
Traces feed a weekly review. Failure modes become new eval cases; the suite grows with the system.
Outcomes
What good looks like
Illustrative targets from engagements of this shape. Yours get agreed up front and measured.
0%
of routine cases closed without a handoff
0.0x
faster cycle time on the target workflow
0%
of agent actions captured in an auditable trace
Toolkit
What we build with
Chosen per engagement against your constraints — never a house stack applied regardless of fit.
Models
- Claude
- OpenAI
- Open-weight (Llama, Mistral)
- Bedrock
- Vertex AI
Orchestration
- Temporal
- LangGraph
- Custom state machines
- Queue-backed workers
Evaluation
- Braintrust
- Langfuse
- Custom scorers
- CI-gated suites
Runtime
- TypeScript
- Python
- Postgres
- Redis
- Kubernetes
Questions
Things clients ask first
Two mechanisms. Irreversible actions sit behind an explicit approval gate that a person clears. Everything else runs under a per-run budget and a circuit breaker that halts the run rather than retrying into a wall. Both are enforced in the tool layer, not in the prompt.
Keep exploring
Related capabilities
Tell us what you are trying to build
A short call with an engineer, not a sales team. If we are not the right fit we will say so and point you somewhere better.