Agentic AI Systems
AI that acts, reasons and operates inside your enterprise stack.
Agentic AI is the next frontier of enterprise automation — systems that don't just answer questions but reason over context, invoke tools, coordinate with other agents and complete multi-step workflows autonomously. TDS Global builds production-grade agentic systems with grounded retrieval, full observability and human-in-the-loop governance.
How we approach agentic ai systems.
Most AI implementations stall at the demo stage because they ignore the hard problems: hallucination at scale, tool reliability, latency in enterprise environments, audit trails, access control and graceful failure handling. Our agentic engineering practice is built around solving these production problems — not the research ones. Every system we ship runs under continuous evaluation, has defined escalation paths and is instrumented for operational monitoring.
Specific deliverables within this capability.
RAG & Knowledge Systems
Retrieval-augmented generation pipelines that ground LLM responses in authoritative enterprise data — PDFs, databases, APIs and structured knowledge bases — with semantic chunking, reranking and citation.
Multi-Agent Orchestration
LangGraph and custom orchestration frameworks that coordinate specialist agents across planning, retrieval, execution and verification roles — with defined failure boundaries.
Tool-Augmented Agents
Agents equipped with verified, sandboxed tool access — database queries, API calls, code execution, document generation — with audit logging and access controls.
Human-in-the-Loop Workflows
Approval gates, escalation logic and override mechanisms that keep human decision authority intact for high-stakes agent actions.
LLM Evaluation Frameworks
Automated evaluation pipelines measuring accuracy, groundedness, latency and safety across model versions — enabling confident updates without regression risk.
AI Observability
Trace logging, cost monitoring, latency dashboards and anomaly alerting that give operations teams full visibility into production AI behaviour.
How we execute every agentic ai systems engagement.
Capability Scoping
We map the target workflow, define agent roles, identify data sources and set measurable success criteria before writing a line of code.
Prototype & Eval Baseline
A working prototype is built rapidly and evaluated against the defined criteria — hallucination rates, task completion, latency — to establish a baseline.
Production Engineering
The prototype is re-engineered for production: error handling, rate limiting, access controls, logging and integration with enterprise identity systems.
Evaluation & Hardening
Continuous evaluation runs in CI. Edge cases are systematically tested. Guardrails are tuned and adversarial inputs are tested.
Deployment & Monitoring
Deployment to cloud infrastructure with observability, alerting and a rollback capability. The system runs monitored from day one.
How you know we do this well.
These are the specific engineering practices and standards that distinguish our work — not claims, but verifiable commitments baked into every engagement.
We build evaluation frameworks before production systems — quality is measurable, not assumed
Every agent has defined tool access scopes, audit logs and human escalation paths
We use LangGraph, custom orchestration and frontier models — selected per use case, not per vendor relationship
Our engineers have implemented RAG systems across document types: contracts, technical manuals, financial reports and clinical records
We design for model versioning — switching underlying LLMs should not require re-engineering the application layer
Observability is non-negotiable: every production AI system we ship has trace logging, cost tracking and latency monitoring from day one
What gets delivered.
Measurable engineering outcomes our practice delivers consistently across client engagements.
Multi-step agentic workflows completing tasks that previously required human analysts
RAG systems achieving >90% groundedness on domain-specific knowledge bases
Evaluation frameworks detecting regression across model updates before production deployment
AI systems operating 24/7 with <0.1% unhandled exception rates
Audit-ready logs satisfying enterprise security and compliance review requirements
Tools & platforms we use.
Bring Agentic AI Systems capability into your organisation.
Our practice leads are available to discuss your specific technical challenges and what a scoped engagement would look like.