Agentic AI

Modernization doesn’t need to be expensive - or disruptive. Torry Harris’ Agentic Modernization-as-a-Service (A-MaaS) uses autonomous AI agents to deconstruct, re-architect, and re-platform your legacy systems - at a fraction of the time and cost.

Agentic Modernization Journey:

• Decompose & document: Agents extract logic, map interdependencies, summarize legacy routines
• Blueprint the target: AI designs phased, cloud-native architectures using real-world telemetry
• Migrate & validate: Agents auto-map data schemas, simulate workloads, and ETL with built-in validation
• Test & optimize: AI generates regression tests and compares legacy vs modern system behavior
• Self-improve: Agents monitor and auto-remediate performance issues post go-live

Accelerators:

• TuringBots: Automate code summaries, re-architecture, ETL, and testing
• AI Factory: Low-cost teams implementing central agent orchestration and knowledge base
• 4Sight: Observability and performance monitoring

Outcome: 50% shorter modernization timelines - 18-month modernization down to 9 months, 30–40% Opex reduction, Zero business disruption via progressive cutover strategy, Explainable AI for risk-aware migration

From requirements to deployment, we - streamline delivery by embedding TuringBots - our proven frameworks like SDLC.ai and Ops.ai are deployed across the development lifecycle with zero drift. These modular agents work autonomously or alongside developers - to shift-left planning, automate code generation, enforce governance, and execute intelligent testing and release orchestration.

Why Traditional SDLC Needs Reinvention

• 30-40% higher design costs due to poor discoverability of software artifacts
• 50% higher dev costs due to lack of automation in code creation and documentation
• 60% slower MTTR due to fragmented ITSM handoffs
• 6000+ redundant DataOps pipelines in typical enterprises driving up costs

Agentic Delivery Flow:

• Parse specs → Auto-generate stories, test plans
• Generate code → Human-in-the-loop review
• Auto-integrate into CI/CD → Deploy & monitor
• Feedback loop → Refine agents using telemetry

Bonus: TuringBots are domain-trained (e.g., telco, banking) and reusable across pipelines.

Outcome: 3× velocity, 50% lower design and documentation effort, 40% increase in developer productivity, 60% higher release confidence, 70% automation coverage across dev and QA

Traditional enterprise testing is too slow, too manual, and too disconnected from development workflows. Torry Harris’ TuringQA brings Agentic AI into the testing lifecycle - enabling real-time test generation, intelligent regression, continuous feedback loops, and domain-trained QA agents that scale with your business.

TuringQA Components:

• Test plan generation
• User scenario modeling
• Coverage tracking across environments
• Auto-validation of test cases
• Defect logging and auto-remediation

Why Choose TuringQA?

Outcome: 70% faster regressions, 40% less QA effort, 50% fewer escaped defects, 100% traceability, Self-learning agents that improve cycle-over-cycle

Modern IT Ops teams are often overwhelmed by fragmented telemetry and mounting SLAs. Torry Harris brings Agentic AI into IT operations through autonomous agents that monitor systems, identify root causes, and remediate issues – with or without human-in-the-loop.

We use AI agents to predict, detect, and resolve issues before they become incidents. Fully integrated with Ops.ai and 4Sight.

What the agents do:

• Monitor telemetry across infra and apps
• Spot anomalies with ML-driven time-series forecasting
• Run auto-resolution playbooks (restart, isolate, escalate)
• Continuously learn from outcomes to reduce noise

Agentic IT Ops Framework:

Outcome: 60% faster incident triage, 80% reduction in false positives, 40% lower MTTR, Always-on improvement via closed-loop learning

Customer expectations are evolving - your AI should, too. Torry Harris deploys AI agents that power contextual, omni-channel customer interactions across voice, chat, email, and apps - trained on real-time data, intent, and sentiment.

How It Works

• Integrates with CRM, ticketing, and ERP systems
• Trained on enterprise context, not just LLMs
• Multi-modal interfaces (voice, chat, visual)
• Learns from past interactions to optimize future ones

AI Agent Capabilities:

Outcome: 2× engagement lift, 60% faster support resolution, 35% reduction in first-response time, 20% increase in self-service resolution, 50% improvement in CSAT scores, 30% uplift in lead qualification via AI sales agents

AI agents are autonomous AI systems that can perceive their environment, make decisions, and take actions to achieve specific goals with minimal human intervention. Unlike traditional AI that simply responds to prompts, agentic AI demonstrates autonomy by planning multi-step tasks, adapting to changing conditions, and learning from outcomes.

The key difference between agentic AI and traditional AI lies in autonomy and decision-making capability. Traditional AI systems are reactive; they process inputs and generate outputs based on predefined rules or patterns, requiring human guidance for each task. Agentic AI, however, operates proactively with goal-oriented behavior, planning sequences of actions and adapting strategies based on environmental feedback.

While generative AI excels at creating content, agentic AI focuses on taking action and driving outcomes. This makes agentic AI particularly valuable for enterprises seeking to automate complex, multi-step processes like supply chain optimization, financial planning, or IT operations management where continuous decision-making is required.

Enterprises can deploy agentic AI by following a strategic framework that addresses both technical and governance requirements. Key steps include:

• Identify high-impact use cases where autonomous decision-making adds value, such as customer service automation, predictive maintenance, or intelligent process orchestration.
• Implement robust governance policies that define decision boundaries, establish human oversight, and ensure alignment with business objectives.
• Address deployment challenges such as data quality, integration with existing systems, and ethical considerations around autonomous actions.

Leverage agentic AI in LLMs to build agents that can understand context, reason through problems, and execute complex workflows. Partner with experienced providers like Torry Harris to ensure scalability, security, and compliance in enterprise deployments.

Torry Harris adopts a business-centric and modular approach to implementing Agentic AI, ensuring that solutions align with each organization’s unique operational context and digital maturity. The process begins with identifying high-value business scenarios where autonomous decision-making can drive measurable outcomes. The team then designs and deploys specialized AI agents built on robust data, integration, and governance frameworks that can perceive, reason, and act within defined business boundaries. By combining domain expertise with advanced AI orchestration and API-led integration, ensuring that each agent is adaptable, scalable, and compliant with enterprise standards.

Torry Harris stands apart through its deep integration expertise, API-first approach, and proven track record in enterprise-scale digital transformation. Unlike generic AI vendors, Torry Harris focuses on building connected intelligence, where agentic AI agents are not isolated tools but part of a cohesive ecosystem that interacts with existing business systems, APIs, and data platforms. The company’s long-standing experience in digital enablement, governance, and automation allows clients to deploy secure, high-performing AI agents that drive real operational efficiency and ROI. Its end-to-end services, from strategy to implementation, ensuring speed, reliability, and continuous innovation.

Agentic AI acts as a catalyst for digital transformation by enabling enterprises to move from reactive automation to proactive, autonomous operations. It empowers organizations to optimize decision-making, reduce manual intervention, and orchestrate complex workflows across departments. By continuously learning from data and outcomes, AI agents enhance agility, improve predictive accuracy, and ensure faster execution of business processes. When integrated with Torry Harris’ digital ecosystem enablement frameworks, Agentic AI helps enterprises achieve greater scalability, innovation, and competitiveness in an increasingly AI-driven market.