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Agentic AI Systems

Multi-agent systems that carry out back-office decisions under policy guardrails, with a human reviewer on the exceptions and an audit trail on every action.

Timeline
4-6 weeks (working agent), 8-12 weeks (production)
Team
architect, MLE, FE, BE, PM; security reviewer on demand
Typical stack
Control plane: LangGraph/Temporal/Argo; retries/backoff/idempotency. Models: OpenAI/Claude/local; routing and fallback policies. HITL: reviewer UI; reason codes; audit and retention windows. Identity: SSO (OIDC/SAML); roles/attributes; least-privilege. On-prem/GovCloud or VPC; secrets in KMS; policy repo in Git.

What you get

  • Orchestration graph in code (agents, tools, policies) with retries/timeouts
  • Evals suite: jailbreak, toxicity, groundedness, accuracy gates
  • Guardrails and safety gates enforced in CI and runtime
  • Reviewer console (approve/annotate/retry) with audit trail
  • Run log and trace viewer (inputs, prompts/versions, tool calls, outputs)
  • Budget caps and alerts; cost per transaction export

Outcomes

  • Eliminate manual back-office steps
  • Policy-aligned actions with human override
  • Traceable, explainable decisions with run logs
  • Reviewer minutes per case ↓ 30-50%
  • Cost per transaction capped and reported

Selected work

What we need from you

  • Process maps or workflow documentation
  • Sample data and edge cases
  • Policy docs, consent and approval thresholds
  • SME availability for evaluation
  • Target KPIs (time saved, accuracy), model/tool constraints

Proof points

  • Before/after KPI chart on target workflow
  • Evals summary with thresholds and deltas
  • Run-log sample (redacted) and trace screenshot
  • Cost per transaction and budget adherence
  • Policy mapping table (policy → runtime check)
  • Critical CVEs at release → 0

Frequently asked questions

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