ZetrixwebDemos That Don't Scale
Prototype agents impress in a demo, then fail on real data, edge cases, and permissions in production systems.
AI Agent Development
AI agent development for enterprise workflows.
We design and build AI agents that plan, call tools, and execute real work in your systems - with human-in-the-loop controls, audit trails, and evaluation baked in from the first sprint.
Trusted by the World Bank & the EU
Tool-calling agents
Human-in-the-loop controls
RAG-grounded answers
Guardrails & audit logs
Continuous evaluation
Agent Readiness
Live
Workflow discovery Aligned
Tool & data design Planned
Guardrail policy Prepared
Evaluation suite Verified
Rollout plan Ready
Definition
What is AI agent development?
AI agent development is the practice of building software systems in which a large language model plans multi-step work, calls tools and APIs, reads and writes data in business systems, and executes workflows toward a defined goal - under explicit guardrails and, where actions are consequential, human approval. Unlike a chatbot that only answers questions, an agent takes actions; unlike a plain LLM integration that generates text, an agent operates inside your systems with scoped permissions, audit logging, and continuous evaluation.
- Planning and task decomposition: the agent breaks a goal into steps and adapts as results come in.
- Tool use: scoped access to APIs, databases, documents, and internal services - never blanket access.
- Grounding via RAG: retrieval over your own documents and data so answers cite real sources.
- Human-in-the-loop gates: approval checkpoints before consequential actions execute.
- Evaluation and observability: test suites, tracing, and audit logs for every agent decision.
Industry Challenges
Why most agent pilots stall before production.
Ungoverned Actions
Agents with broad system access and no approval gates are a security and compliance risk no enterprise can accept.
No Way to Measure Quality
Without evaluation suites, teams can't tell whether an agent got better or worse after a model or prompt change.
Solution Blueprint
Agents engineered like production software.
Workflow-First Design
We start from one measurable business workflow, then design the agent's tools, data access, and success criteria around it.
Scoped Tools & Guardrails
Least-privilege tool permissions, validation on every input and output, and human approval before consequential actions.
Evaluation & Observability
Regression eval suites, step-level tracing, and audit logs so quality is measured, not assumed, release after release.
Outcome Signals
Working software, not a science project.
Hours back per workflow
Agents absorb the repetitive middle of workflows while people keep the judgment calls.
Auditable by design
Every agent step is logged and traceable - answers for security, compliance, and leadership alike.
Quality you can prove
Eval scores over time show exactly how the agent performs before it touches production data.
Start smaller: our AI Acceleration Studio runs scoped agent pilots, and our AI & GenAI Solutions page covers the broader practice.
Use Cases
Where enterprise agents earn their keep.
Support & Service Ops
Agents that triage tickets, draft grounded responses, and execute resolution steps with approval gates.
Back-Office Workflows
Document processing, data reconciliation, compliance checks, and reporting across internal systems.
Engineering Copilots
Internal agents for code review support, test generation, release notes, and operational runbooks.
Related reading: AI & GenAI solutions, enterprise AI implementation, our AI trust center, and the AI glossary for terms like RAG, LLM, and agent.
FAQs
Frequently asked questions.
A chatbot answers questions in a conversation. An AI agent goes further: it plans multi-step work, calls tools and APIs, reads and writes data in your systems, and executes workflows toward a goal - with checkpoints where a human reviews or approves. A plain LLM integration generates text; an agent takes actions.
No - they combine. Retrieval-augmented generation (RAG) grounds an agent's answers in your documents and data. Agents typically use RAG as one of several tools alongside API calls, database queries, and workflow actions. Most production agent systems we build include a retrieval layer.
Through guardrails designed into the architecture: scoped tool permissions, human-in-the-loop approval for consequential actions, input/output validation, full audit logging of every agent step, and evaluation suites that test agent behavior before and after each release.
A scoped pilot agent - one workflow, defined tools, human approval gates - typically ships in 6-10 weeks. Expanding to multiple workflows with mature evaluation and monitoring usually runs one to two quarters, depending on integration complexity.
Have a workflow an agent should own?
We'll scope a pilot with guardrails, evaluation, and a clear success metric.