Agents that finish the work.
We design and ship autonomous AI systems — agents that plan, use your tools and complete real work in production, with the tracing, evaluation and guardrails that make autonomy safe to trust.
A model answers.An agent finishes.
Most AI work stops at the demo. A convincing conversation, a screenshot in a deck, and nothing that survives a Monday. The gap is not intelligence — the models are already good enough. The gap is engineering.
We build the other ninety percent: the tool contracts, the state machines, the evaluation suites, the guardrails, the traces that tell you why an agent did what it did at 3am. Autonomy is an infrastructure problem wearing an AI costume.
- nava
- new
- sena
- force, army
a new force — an army of agents working in formation
Four disciplines, one delivery team.
Autonomy is rarely one problem. These are the layers we build, and a system that survives production usually needs more than one of them at once.
- 01
Agentic Systems
Multi-agent architectures that plan, delegate and finish work instead of just answering.
- Orchestration
- Planning loops
- MCP
- Tool use
- State machines
- 02
Autonomous Workflows
End-to-end business processes handed to agents, with humans kept at the decisions that matter.
- Back-office
- Operations
- Approvals
- Straight-through processing
- 03
Applied Intelligence
Retrieval, reasoning and domain knowledge wired into agents that answer with receipts.
- RAG
- Knowledge graphs
- Embeddings
- Grounding
- 04
Agent Infrastructure
The unglamorous layer that decides whether an agent survives contact with production.
- Observability
- Evals
- Guardrails
- MLOps
Reversible steps, in order.
Four phases, each ending in something you can evaluate. Nothing goes autonomous until the numbers from the previous phase justify it.
- 01 — Signal1–2 weeks
Find the work worth automating
We sit with the people doing the process today and map it step by step, including the exceptions nobody documented. Each candidate is scored on value against feasibility, and we say plainly which ones are not worth an agent.
- Outputs
- Process map
- Opportunity scoring
- Feasibility verdict
- Target metrics
- 02 — Blueprint2–3 weeks
Design the topology, prove the risk away
Agent roles, tool contracts, memory, escalation paths and the guardrail model are designed on paper, then the single riskiest assumption is prototyped end-to-end. We would rather kill an idea in week three than in month six.
- Outputs
- Agent architecture
- Tool contracts
- Riskiest-assumption spike
- Eval plan
- 03 — Build6–16 weeks
Ship it instrumented, in shadow first
Two-week increments, each ending in something running against real data. Tracing, evals and cost budgets land with the first agent rather than after it. Shadow mode runs alongside the humans until the numbers justify turning autonomy on.
- Outputs
- Production system
- Eval + trace suite
- Shadow-mode scorecard
- Runbooks
- 04 — AutonomyOngoing
Widen the scope, then hand over the keys
Autonomy expands per category as agreement rates allow. Escalations become new eval cases, and your engineers take increasing ownership through paired delivery until the handover is a formality rather than an event.
- Outputs
- Staged rollout
- Drift monitoring
- Team enablement
- Handover package