AI Agents
Software that decides, not just answers
An agent is only useful when it can act. We build AI agents that hold a goal, choose tools, call your systems through typed interfaces, recover from failure and stop when they should. Every run is traced end to end, every tool call is permissioned, and every failure mode has a defined fallback — which is the difference between a demo and something you can put in front of customers.
$ run --trace --budget 0.05
14:02:11.204plandecompose → 3 steps124ms
14:02:11.328retrievepolicy/returns#4 · score 0.91318ms
14:02:11.646tool:crmgetOrder(#48210) → shipped412ms
14:02:12.058tool:omscreateReturn(#48210) → RMA-7741377ms
14:02:12.435verifygrounded ✓ · citations 2/2 · policy ✓88ms
14:02:12.523respondstreamed 412 tokens · $0.0041.2s
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- Agent architecture: goals, tools, memory, termination and escalation rules
- Typed tool layer over your APIs, database and third-party services
- Run traces with cost, latency and token accounting per step
- Human-in-the-loop approval gates for irreversible actions
- Budget and rate limits so a loop cannot run up your bill
- Eval suite covering the paths that matter before anything ships
- 01
Map the job
We write down the task a human does today, including what they check and when they escalate.
- 02
Constrain the tools
Each capability becomes a typed, permissioned tool with its own failure behaviour.
- 03
Measure before shipping
An eval set built from real cases gates every release, so quality is a number rather than a feeling.
- 04
Run it observably
Traces, cost dashboards and alerting go live with the agent, not months later.
A chatbot answers. An agent acts: it plans a sequence of steps, calls tools and APIs to change state in your systems, checks the result and either finishes or escalates. Chatbots need good retrieval; agents additionally need permissions, idempotency, budgets and traces.
Three layers: tools are permissioned and typed so an agent can only do what it was granted; irreversible actions pass through a human approval gate; and every run carries hard budget, step and time limits that terminate it. All of it is logged.
A scoped production agent covering one workflow typically takes six to ten weeks including evals and observability. A proof of concept against real data takes two to three weeks.