AI agents that
do the work,
not just describe it.
We build autonomous agents that plan, execute, and adapt across your real systems. Production-grade in 2–4 weeks, with human-in-the-loop controls, full observability, and a clear audit trail on every action.
Agents reason, use tools,
and finish the task.
A chatbot answers a question. An agent plans a sequence, calls APIs, handles exceptions, escalates when it's unsure, and leaves a full trace. The engineering is different. So are the outcomes.
What an Optivus agent
actually does.
Six engineering patterns we bring to every agent engagement. Not a menu. A stack.
Agents that decompose, decide, and self-correct.
Decomposition, tool selection, branching, and self-correction. The agent reasons about the approach, revises when things go sideways, and surfaces its thinking so you can see why.
- ✓Task decomposition & sub-goals
- ✓Self-correction on failure
- ✓Explicit confidence thresholds
Connected to your real systems, not a sandbox.
APIs, databases, document stores, internal services. Every tool call is schema-validated, rate-limited, and logged. Adding a new tool is config, not a rewrite.
- ✓REST · GraphQL · gRPC · SQL
- ✓Retry & backoff, per-tool
- ✓Tool registry & versioning
Approval gates, exactly where you need them.
Configurable policy: auto-run low-risk actions, escalate high-stakes ones to a human. Every approval is captured with context and the full trace, so nothing is decided in a black box.
- ✓Policy-based escalation
- ✓Inline approval UI
- ✓Every decision auditable
See exactly what the agent did, and why.
Structured traces of every plan, tool call, and result. Latency, cost, and failure modes broken down per run. When something goes wrong, we already have the evidence.
- ✓Per-run trace & replay
- ✓Cost · latency · failure dashboards
- ✓Alerts on drift & anomalies
Coordinated teams of specialist agents.
An orchestrator routes sub-tasks to specialists: a research agent, an extraction agent, a decision agent. Each is small, testable, and owns one job well.
- ✓Orchestrator / worker pattern
- ✓Message contracts between agents
- ✓Per-agent eval suites
Regression tests for non-deterministic systems.
Scenario-based eval suites that run on every change: happy path, missing fields, ambiguous intent, adversarial inputs, edge cases. We only ship when the matrix is green.
- ✓Scenario coverage matrix
- ✓Regression gates in CI
- ✓LLM-judge + golden sets
The same engineering
that powers our own products.
We don't just consult. FlowFin, Janus, and Veritas are production agent systems we built. Same team, same patterns you'll get.
30+ AI agents across finance operations: invoice extraction, 3-way matching, report generation, anomaly detection. The same patterns we ship for enterprise clients.
Visit FlowFin1,000+ résumés screened daily, 60+ recruiters in production. Candidate matching, outreach drafting, interview scheduling: an agent per sub-task, coordinated end-to-end.
Visit JanusKnowledge-grounded content generation with mandatory citation. 12+ content types, every claim traceable to a source document. Agents that cannot hallucinate silently.
Visit VeritasThe Optivus Method.
Every engagement runs four phases on a fixed rhythm. You always know what is being delivered, what comes next, and where we are on the clock.
Map the workflow, the tools, the escalation points. Define success metrics.
Iterative development, weekly demos. Start with the core workflow, add tool integrations, refine behavior.
Deploy with full observability. Every action logged, alerts on anomalies, team trained.
Analyze usage patterns, expand to adjacent workflows, optimize tool calls.
The underlying engineering transfers.
Planning, tools, guardrails, and evals are the same pieces. What changes is the workflow.