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Agentic AI

AI agents that survive contact with production.

Anyone can demo an agent. We ship agents that are retrieval-grounded, tool-constrained, evaluated like software, and governed like they handle your customers' data — because they do.

3 layers grounding, policy, verification — no single layer trusted alone
6 wks Agent Deployment Accelerator: one scoped agent, production-grade
100% of agent actions logged, auditable, and rollback-planned
Production agent loopProduction agent loop: user request goes to the agent, which retrieves grounded context from the RAG layer, uses constrained tools, and is wrapped by evals and guardrails before producing a governed response. AnovaCloud production agent loop request ground act USER employee · customer authenticated RAG LAYER your governed data citations, not vibes AGENT planner · memory constrained tools TOOLS APIs · search · code least-privilege scopes EVALS + GUARDRAILS regression tests · policy · audit FIG. 01 — THE PRODUCTION AGENT LOOP

Every agent we ship runs inside this loop — no exceptions.

What we do

Agentic AI offerings

01

Enterprise RAG

Search and synthesis over your documents, tickets, and wikis — with citations, access controls, and freshness guarantees.

  • Chunking, embedding, and reranking tuned to your corpus — measured, not guessed
  • Row- and document-level access control inherited from source systems
  • Eval sets built from your real questions, run on every change
Read the RAG pattern →
02

Multi-Agent Systems

For workflows too complex for one agent: planner-executor-critic topologies with handoffs you can trace.

  • Decomposed workflows: research, drafting, review, and action as separate agents
  • Human-in-the-loop gates on irreversible or high-stakes actions
  • Full traceability — every handoff logged and replayable
Read the multi-agent pattern →
03

Agent Deployment Accelerator — 6 weeks

Our productized path from "interesting demo" to "running in production": one scoped agent, fully governed, in six weeks.

  • Week 1–2: workflow scoping, data grounding, success criteria
  • Week 3–4: agent build, tool integration, eval harness
  • Week 5–6: guardrails, observability, pilot launch, handover runbook
Scope your accelerator →
04

Evals & AI Governance

The unglamorous layer that decides whether agents survive: evaluation suites, policy enforcement, and audit trails.

  • Golden datasets and regression suites for every agent behavior
  • Guardrail policies: allowed tools, data boundaries, escalation rules
  • Audit logging and red-teaming before launch — and after every model swap
Govern your agents →

Going deeper? Our flagship Agentic AI Workforce practice designs the full human-plus-agent operating model — not just the agents, but the teams, rituals, and accountability around them.

Engagement models

How agentic AI engages

ModelFormatBest for
Accelerator 6 weeks, fixed scope First production agent with full governance
Agent program Quarterly, sequenced agents Portfolios of agents across functions
Eval & governance retrofit 3–4 weeks Existing pilots that need to become production-safe

FAQ

Agentic AI, answered

Production agents are retrieval-grounded with citations, run inside an eval harness with regression tests, enforce guardrail policies on every action, log every decision for audit, and have rollback plans. Demos skip all of that. We build the latter.
Model-agnostic by design: we architect for swapping frontier models without re-platforming, and pick orchestration frameworks based on the workflow's complexity — not fashion. Your data stays in your boundary.
Three layers: grounding (retrieval with citations over your governed data), policy (guardrails constraining tools, data access, and tone), and verification (eval suites plus human-in-the-loop on high-stakes actions). No single layer is trusted alone.
One scoped production agent: workflow design, RAG over your data, tool integrations, an eval harness, guardrail policies, observability, and a runbook with handover to your team.
Our thesis is humans + AI workforce: agents take the repetitive, research-heavy, and coordination work so your people do judgment work. See our Agentic AI Workforce practice for the operating model.

Start here

Ship one agent right. Then scale the pattern.

Six weeks, one workflow, full governance. Tell us which workflow deserves it first.

Talk to an Architect Agentic AI Discovery Workshop