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
Prior authorization: 4.2 days → 6 hours.
5-agent clinical workflow: intake, clinical review, policy check, approval, notification. PII redaction and HIPAA audit trails enforced at the orchestration layer. On-prem deployment with SSO for 1,200 clinicians.
Multi-agent · HIPAA · On-prem
Planning cycle: 3 days → 4 hours.
6-agent supply-chain planner: demand forecasting, inventory optimization, supplier coordination, logistics routing, risk assessment. Expedited shipping ↓38%. Executives get real-time visibility into agent decision logic.
Multi-agent · Forecasting · Supply chain
Inspect the execution rules
Before defining an agent workflow, identify who can request an action, who approves it and how an uncertain tool response is resolved.
- Run the approval and retry example to inspect authorization, expiring approvals and duplicate calls in a synthetic local workflow.
- Read the service-sector automation overview for context on the process being automated.
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
Scoped builds, priced per workflow rather than per seat. A single well-bounded workflow (invoice triage, lead qualification, support escalation) is a smaller engagement than a multi-department rollout, and we quote after mapping the workflow, its systems, and the cost of a wrong action. Ongoing costs are model usage and monitoring, which we cap with routing and budget guardrails so the bill scales with the value delivered, not with traffic surprises.
Start where volume is high, the rules are mostly clear, the outcome is measurable, and a wrong action is cheap to catch. Invoice matching beats contract negotiation; ticket triage beats customer apologies. The first workflow should prove value in weeks and build the review habits your team needs, because the second and third workflows get approved on the evidence the first one produces.
They take over tasks, not roles. What changes in practice: people stop doing the repetitive middle of a process and move to its edges, defining the rules, reviewing exceptions, and handling the cases that need judgment. The teams that get the most from agents redesign roles around that review work instead of pretending nothing changes. We are direct about this during scoping, because pretending otherwise is how adoption fails.
Four to six weeks for a working agent that handles real decisions in a controlled way. Eight to twelve weeks until the agent is production-ready and handling volume without human oversight. The first timeline assumes you have clean data, clear decision rules, and systems we can integrate with. Some projects need an extra month of integration work or an extra phase to sort out governance questions with your legal team.
Traditional automation follows rigid decision trees. If X, then Y. It breaks when the world doesn't match the tree. Agentic AI reasons about situations, gathers context, evaluates options, makes decisions based on patterns it's learned, and can handle scenarios the original programmers didn't anticipate. Traditional automation fails hard when things are unclear. Agentic AI degrades gracefully: it either surfaces the unclear case for human judgment or applies its best reasoning and logs its confidence level so you can review it.
Yes. We've integrated agents with systems built in 1997. The integration takes time because those systems were not designed to be integration points. But if humans can use it, agents can interact with it. We pull data via APIs where available, database queries where needed, scraping as a last resort. Most of your integration time is getting permission to connect agents to those systems and understanding what the data actually means, not the technical plumbing.
You catch it through monitoring, you reverse it, and you improve the agent. Agents operating at 95% accuracy with human review on outliers are normal. The question is whether that 95% is better than your current human accuracy, and whether the cost of the 5% error is lower than the cost of the human reviewing everything. We build monitoring to catch errors quickly and shift human effort from routine processing to quality review.