![\"\"](\"https:\/\/blogs.infosys.com\/topaz\/wp-content\/uploads\/2023\/12\/artificial-intelligence-g263d48987_1920-300x100.jpg\")
<\/p>\n<\/p>\n
Enterprise AI delivers meaningful value only when promising ideas become reliable, trusted solutions in production. Yet moving from a successful pilot to a solution that works within real business processes is often the hardest part. Infosys applies a Forward Deployed Engineering (FDE) approach on AWS to bridge this gap. Working alongside client teams and within their data, systems, and governance requirements, Infosys engineers take a clearly defined business problem from discovery to production\u2014and turn what they learn into reusable assets for future initiatives.<\/p>\n
The challenge is rarely just the AI model. Enterprise processes cross applications, policies, and approval boundaries. Access to data and tools must be secured, agent behavior evaluated, and performance continuously monitored. Without a consistent approach, teams end up rebuilding the same foundations for every new use case. As part of Infosys Topaz<\/a>, Infosys FDE brings these needs together through three complementary capabilities: Agentic Transformation, AI-Driven Development Lifecycle, and Harness Engineering.<\/p>\n A practical path from discovery to production<\/strong><\/p>\n An FDE engagement starts by establishing the conditions for a credible production outcome: a committed client sponsor, a measurable business goal, an approved path to relevant data and systems, and an acceptable security and Responsible AI approach. With these foundations in place, the team progressed through four phases:<\/p>\n Discover and Ground:<\/strong> Validate the business problem, establish baselines, understand the client\u2019s data, domain rules, and policies, and identify the smallest viable production release.<\/p>\n Build and Orchestrate:<\/strong> Deliver an end-to-end slice early, then integrate the necessary tools, knowledge, workflows, memory, and approval controls using AI-driven engineering practices.<\/p>\n Prove and Release:<\/strong> Test quality, safety, security, performance, and cost against agreed thresholds before deploying through approved change-management processes.<\/p>\n Operate and Compound:<\/strong> Transition the solution to operations, measure business outcomes, and capture approved patterns, tests, and runbooks for reuse.<\/p>\n Human oversight is built into the process rather than added at the end. Approval, exception-handling, and escalation points are defined before agents take autonomous actions. Infosys targets an initial production release within 45\u201390 days of the FDE program\u2019s start, subject to the engagement\u2019s scope and readiness.<\/p>\n Agentic Transformation: Redesigning how work gets done\u00a0<\/strong><\/p>\n The first capability focuses on the business process itself. Agentic Transformation applies AI agents where work requires context, planning, access to tools, and controlled action. The goal is not simply to introduce another chatbot; it is to rethink how people and agents collaborate while keeping responsibilities and decisions clear.<\/p>\n Infosys begins by assessing potential use cases against business value, suitability, data readiness, integration effort, risk, and scalability. Joint teams then redesign priority workflows and develop agents using Amazon Bedrock and Amazon Bedrock AgentCore, connecting them to the client\u2019s knowledge sources, APIs, and applications. The result is more than a working agent: clients gain a prioritized opportunity portfolio, redesigned workflows with explicit approval points, identity and policy controls, observability dashboards, and reusable integration patterns.<\/p>\n For a quick-service restaurant (QSR) brand, multiple functions including legal, operations, real estate, licensing, and communications were running separate AI pilots. Each recreating capabilities such as model access, data pipelines, identity, orchestration, and monitoring. Infosys consolidated these efforts into a multi-agent, multi-model platform on AWS, supported by shared identity, memory, and observability services. A hybrid knowledge layer used domain-aware document chunking\u2014for example, at the clause level for legal content\u2014while scoped access kept sensitive legal information separate from other domains. In internal benchmarks, tiered model routing reduced inference costs by approximately 60%. Contract review time fell from 2\u20134 hours to 2\u20134 minutes, franchise licensing queries dropped from more than 30 minutes to under 30 seconds with source citations, and internally observed availability remained above 99.9%.<\/p>\n AI-Driven Development Lifecycle: Building with speed and discipline\u00a0<\/strong><\/p>\n Once a process has been redesigned, the next challenge is building and evolving the software behind it. The AI-Driven Development Lifecycle brings AI assistance into requirements, design, coding, testing, security, deployment, modernization, and operations. Using Amazon Q Developer, Kiro, and AWS developer services,\u00a0 along with Infosys Topaz, teams can accelerate delivery while retaining human accountability for architecture, security, and production decisions.<\/p>\n For example, a leading foodservice provider needed a centralized invoicing platform that could coexist with legacy systems while supporting more than 500 concurrent users and over 10,000 invoices per day. Infosys used specification-driven development with Kiro, storing structured specifications in Git to guide code generation and maintain traceability. Where real-time synchronization was needed without AWS DMS, the team implemented an event-driven framework using Amazon EventBridge, AWS Lambda, and AWS Step Functions. Unit test coverage reached 80%, while the client reported 40% faster time to market, 30% fewer defects, and 80% less code rework.<\/p>\n Harness Engineering: Making agents trustworthy at scale\u00a0<\/strong><\/p>\n Speed alone is not enough when agents can interact with enterprise systems. Harness Engineering provides the controlled environment in which agents are developed, tested, secured, observed, and continuously improved. It makes behavior measurable and repeatable by governing access to models, tools, knowledge, and identities; testing routine, edge, adversarial, and failure scenarios; and retaining evidence for governance and audit teams.<\/p>\n Infosys works with business, security, and governance stakeholders to define success criteria for each agent. Teams then build the harness using Amazon Bedrock AgentCore, Bedrock evaluation and guardrail capabilities, and AWS monitoring services. By integrating these checks into CI\/CD pipelines, they can evaluate changes before release. Deliverables include evaluation frameworks, curated test datasets, automated quality gates, observability dashboards, incident runbooks, and production-readiness evidence.<\/p>\n A collaboration with leading Bank illustrates this approach with Agent Development Lifecycle patterns on Amazon Bedrock AgentCore. During development, agents use standard templates, pass through enterprise CI\/CD and automated security scanning, and register metadata in a central Agent Registry. At runtime, each agent has a verifiable identity, a trust boundary separates reasoning from enterprise systems, and tool access is restricted to approved actions. Agents progress through gated stages\u2014from sandbox testing with stubbed tools and adversarial scenarios, to govern access to real tools, and finally to production\u2014with reasoning steps and tool calls traceable throughout. The QSR platform provides another example of operational visibility: per-agent cost tracking and end-to-end tracing helped surface cost anomalies and latency spikes before they affected users at scale.<\/p>\n Start with one bottleneck\u2014and build from there\u00a0<\/strong><\/p>\n These three capabilities are most powerful when used together. Agentic Transformation identifies where agents can improve a process and defines how people remain in control. The AI-Driven Development Lifecycle helps teams build and change those solutions efficiently. Harness Engineering provides the testing, safeguards, and operational evidence needed to release and run them responsibly.<\/p>\n Rather than attempting a sweeping transformation from day one, organizations can begin with one high-value bottleneck, one authorized scope, and a measurable baseline. FDE teams at Infosys can then work alongside client stakeholders to demonstrate value in production, operate within established controls, and carry forward reusable assets that make the next use case easier to deliver. Using a Forward Deployed Engineering approach on AWS, Infosys teams can work alongside client stakeholders to deliver value in production, operate within established controls, and create reusable assets that make each subsequent use case faster and easier to deliver.<\/p>\n References:
\n[1] Scaling Agentic AI: How Infosys Built a Multi-Agent Platform on AWS: https:\/\/www.infosys.com\/services\/data-ai-topaz\/insights\/scaling-agentic-ai.pdf\u00a0<\/a><\/p>\n