AI Agent & Agentic AI
Multi-agent systems engineered for production — not the demo. Orchestration, governance, observability, and the operational layer that keeps agents running long after launch.
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The Production Reality
Gartner projects 40% of enterprise applications will feature task-specific AI agents by year-end 2026. The same research projects 40% of agentic AI projects will be cancelled by 2027 — failing on cost, governance, or unclear ROI. Agentic AI is not a model — it's a system. Most agentic AI pilots fail because they're wired together with brittle prompt chains, no orchestration layer, no observability between agents, and no policy layer governing what each agent can do. They work for 60 days. They break in production. The QSS Thesis We treat agent orchestration with the same rigor we treat microservices — bounded contexts, contract tests, observability between agents, and a clear policy layer governing every action. The model is 10% of the work. The orchestration, governance, and operational layer is the other 90% — and that's where we engineer.
What QSS Delivers — 7 Layers of Agentic AI Engineering
Agent Design & Architecture
Single-agent vs. multi-agent decision framework. Bounded context design. Tool selection. Memory architecture. Reasoning vs. action separation.
Multi-Agent Orchestration
Orchestration framework selection (LangGraph, CrewAI, AutoGen, custom). Inter-agent contracts. Failure-mode handling. Concurrency and rate-limit management.
Tool & API Integration
Secure tool-use patterns. MCP (Model Context Protocol) integration. API authentication and rate limiting. Action-result validation. Idempotency for write operations.
Policy & Governance Layer
Tiered approval workflows. Action whitelisting. PHI/PII handling. Cost ceilings. Audit trail for every agent decision.
Observability & Monitoring
Per-agent latency and cost tracking. Drift detection. Reasoning trace logging. Tool-call success rates.
Production Deployment & Operations
Sandboxed CI environments. Shadow deployment. Gradual rollout. Named on-call ownership.
Continuous Improvement Layer
Eval harness for agent behavior. A/B testing across agent versions. Feedback loops for prompt and tool refinement.
The QSS Approach — Phased Engagement
Phase 1: Use Case Validation & Architecture (Weeks 1–3)
Single-agent vs. multi-agent decision. Bounded context mapping. Tool inventory. Cost-per-outcome modeling. Sprint 1 Architecture Contract signed before any code is written.
Phase 2: Agent Engineering & Tool Integration (Weeks 3–10)
Agent prompts engineered with eval-driven iteration. Tool integrations built with idempotency, retry logic, and audit logging. Inter-agent contracts implemented.
Phase 3: Governance, Observability & Safety Engineering (Weeks 6–14)
Policy-as-code controls applied. Audit-trail infrastructure deployed. Observability dashboards built. Incident response playbook written.
Phase 4: Pilot, Validation & Hardening (Weeks 12–18)
Shadow deployment alongside human workflows. Eval harness measuring accuracy, cost, latency. Adversarial testing for prompt injection and tool misuse.
Phase 5: Production Rollout & Handover (Weeks 18–24)
Gradual rollout to production traffic. Named on-call ownership. Training for internal operations teams. Quarterly re-evaluation cadence established.
Industry Applications
Use Case
What the Agent Does
Healthcare Prior Authorization
Multi-agent system reviews clinical documentation, applies payer rules, routes complex cases to humans, generates audit trails
BFSI Claims & Underwriting
Agent workflow validates claim data, cross-references policy terms, identifies fraud signals, escalates edge cases
Customer Support Resolution
Tier-1 resolution agent + escalation agent + knowledge synthesis agent + QA agent in coordinated workflow
Software Engineering Agents
Code review, bug triage, documentation generation, test authoring — with senior-engineer sign-off gates
Engagement Options
Single-agent MVP
$80K–$180K over 12–14 weeks
Multi-agent system
$200K–$450K over 16–22 weeks
Enterprise platform
$450K–$800K over 20–24 weeks
Why QSS for Agentic AI
Multi-agent production experience
not just prototype demos. Live engagements in healthcare, BFSI, and customer-support workflows.
OWASP Top 10 for Agentic Applications 2026 implementation across every agent engagement
Policy-as-code engineering
governance shipped from Sprint 1, not after the first incident
Senior AI engineers with hands-on LangGraph, CrewAI, AutoGen, and custom orchestration experience
Cost-per-outcome focus
we measure agentic ROI as business outcome per dollar, not token efficiency
Frequently Asked Questions
An AI agent is a single autonomous unit; agentic AI is the system architecture for multiple agents working together. A chatbot is an agent. A claims-adjudication workflow with three coordinated agents handling different tasks is agentic AI.
Use multi-agent when the task naturally decomposes into specialized sub-tasks with different reasoning patterns. Single-agent is simpler, cheaper, easier to govern. Multi-agent only wins when the decomposition genuinely improves quality or modularity.
Three layers: bounded context, policy-as-code, and human-in-the-loop gates. Each agent has a narrow scope. Every action is validated against policy before execution. High-risk actions require named human approval. Plus continuous observability — drift detection paged to on-call.
Single-agent MVP: $80K–$180K over 12–14 weeks. Multi-agent system: $200K–$450K over 16–22 weeks. Enterprise platform: $450K–$800K over 20–24 weeks. Ongoing operations: 15–25% of build cost annually.
Cascading inference architecture. Cheap fast models for routine sub-tasks; reasoning models reserved for the genuinely complex decisions. Most well-architected agent systems run at 1.5–2× the cost of standard LLM architectures, not 5–10×.
Three things engineered before the first incident: action reversibility for write operations, named on-call ownership for incident response, and a pre-tested incident response playbook that includes regulator notification when required. Engineer Agentic AI for Production — Not for the Demo Book a 30-minute architecture review with QSS's agentic AI engineers. We'll walk through your use case, the single-agent vs. multi-agent decision, and what a 12–24 week engagement would deliver. Book a 30-Min Agent Architecture Review →
Ready to Build Agents That Survive Production?
Tell us the workflow you want to automate — our senior engineers will map the orchestration, governance, and observability to get your agentic AI to production.
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