Sold by: SoftwareOne
SoftwareOne's Forward Deployed Engineering (FDE) delivers production-ready agentic AI systems on AWS in 45–90 days, with measurable accuracy thresholds, automated release gates, and built-in guardrails - not demos or proofs of concept. Specialist pods with AWS FDE credentials use a six-step evaluation method with ground-truth test cases built alongside your domain experts, ensuring agent accuracy you can measure and trust. Ongoing production monitoring, cost and latency controls, and a full operational handover ensure your agentic system stays reliable, safe, and cost-effective long after go-live.
Overview
SoftwareOne Forward Deployed Engineering (FDE) is a structured delivery service that takes agentic AI systems from qualified opportunity to monitored production on AWS - typically within 45 to 90 days.
The Problem: Most enterprise AI initiatives stall at the demo or proof-of-concept stage. When they do reach production, organisations struggle to measure agent accuracy, control costs, enforce safety guardrails, and retain operational knowledge after the delivery team leaves. FDE solves all four problems through a rigorous, evidence-based methodology.
How It Works: Every engagement follows a four-phase lifecycle with clear entry and exit criteria at each gate:
Discover and Ground - Confirm the business case, map data sources and target systems, build ground-truth test cases with the customer's domain expert, and agree on accuracy thresholds before any code is written.
Build and Orchestrate - A specialist pod of 2–6 engineers (at least two holding current AWS FDE credentials) builds the agentic system using the AI-DLC method. The pod covers three disciplines: data, integration and ontology; agent orchestration; and evaluation and governance.
Prove and Release - Evaluate the system against the ground-truth test set, calibrate LLM judges against human expert labels, and pass automated release gates before going live. Guardrails and decision boundaries block unsafe actions — they do not just log them. Releases follow a staged rollout: evaluate, canary, A/B check, with CloudWatch-triggered rollback ready from day one.
Operate and Compound - Hand over the system with a full runbook, know-how transfer sessions, and production monitoring. Measure outcomes against the baseline captured in Phase 1. Reusable assets are approved by the Delivery Head and added to the harness catalog, reducing time and cost on future engagements.
AWS Services: Built natively on Amazon Bedrock AgentCore (runtime, evaluations, observability), Amazon Bedrock Guardrails, Amazon CloudWatch, and AWS CloudTrail.
Highlights
- Evaluation Methodology: A six-step evaluation method runs across the entire lifecycle: Agree on success criteria → Build ground-truth test cases → Calibrate LLM judges → Gate releases below thresholds → Release in stages → Watch production with ongoing feedback loops. Accuracy thresholds are set per severity tier: zero failures on critical tasks, 95–100% pass rate on regression flows, and baseline-driven thresholds on quality cases. Results are reported with 95% confidence intervals.
- Production Monitoring: After release, the system collects OpenTelemetry traces via AgentCore Observability, tracks cost by agent and tenant using Bedrock inference profiles, and monitors failure signals including tool errors, looping sessions, guardrail blocks, and user feedback. A weekly review covers 20–50 flagged sessions, and confirmed failures are added back to the test set for continuous improvement. Monthly reports provide full audit evidence retained for at least six months.
- Qualification Gate: Before any pricing or timeline is committed, each opportunity must pass three tests: (1) the work requires a production agentic system with multi-step reasoning, API actions, and human approval points; (2) the customer confirms a business case with a named owner, baseline metrics, measurable outcomes, and allocated funding; (3) the first production release is achievable in 45–90 days with confirmed data access, security approvals, and customer participation.
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