Agentic AI Services for the Enterprise

Agent engineering in 2026 is not just prompts and tools. It is context engineering, evaluation harnesses, the right runtime for the workload (Google ADK, Anthropic Claude SDK, OpenAI Assistants, AWS Bedrock, Azure AI Foundry), and a deliberate choice between single-agent and multi-agent topology. We make those choices on first principles, not on whatever the vendor pushed last week.

The integration surface matters as much as the agent. Custom MCP servers connect agents to your data and tools without coupling you to a vendor agent runtime. Skills, on the Anthropic open standard, give agents repeatable workflows that the recruiter or analyst can tune without touching code. Multi-agent orchestration via the A2A protocol lets specialised agents coordinate without one becoming a god class.

Evaluation is the discipline most pilots skip. A demo evaluated against five hand-picked inputs tells you nothing about production. We design evaluation harnesses that capture the failure shapes the business actually cares about: hallucination rates per intent, tool-call accuracy per integration, end-to-end success rates per workflow. The harness runs continuously, not just at release time, because the world the agent operates in keeps changing.

Human-in-the-loop is not a fallback. It is a design pattern. The interesting agentic systems put humans in specific places: approving consequential actions, resolving ambiguity the agent flags rather than guesses through, and supplying corrections that retrain the system over time. We design those touchpoints into the architecture, not as a defeat condition but as the operating model.

Production reliability comes from the operating layer underneath. Observability, drift detection, token-cost optimisation, audit logging. We carry fifteen years of MLOps DNA forward into AgentOps; agentic-native firms appearing since 2024 do not have that lineage. Models are now decisions, and decisions need rollback.