Autonomous AI agents, safely in production
AI agent development services for the enterprise — reasoning loops, tool use, multi-agent orchestration, memory and human-in-the-loop control, with the guardrails and observability to run agents for real.
Quick answerAI agent development builds AI systems that plan, use tools and take multi-step actions toward a goal, rather than answering a single prompt. It covers the agent’s reasoning loop, tool and API integrations, memory, orchestration of one or many agents, guardrails, evaluation, and the observability needed to run agents in production — where the risk is not a wrong answer but a wrong action.
From a single agent to a supervised fleet
Each links to a dedicated page.
Agentic AI Development Services
Multi-agent systems, planners and executors, tool orchestration, shared memory and governance.
ExploreAI Agent Integration Services
Connect agents to your systems, APIs and identity with typed tools, allow-lists and approvals.
ExploreAgent design & evaluation
Reasoning loop design, scenario-based evaluation, adversarial and failure-mode testing.
ExploreGuardrails & observability
Allow-listed actions, spend and rate limits, full step tracing, kill switch and rollback.
ExploreHuman-in-the-loop
Approval gates and review for anything that changes data, money or systems.
ExploreEnterprise AI Services
Agents at scale — one governance model, one platform, one operating model.
ExploreWhat an agent build involves
Reasoning loop & planning
Goal decomposition, next-action selection, retries and recovery from unexpected states.
Tools & integrations
Strictly-typed tools into your APIs and systems, scoped to least privilege.
Memory & orchestration
Short- and long-term memory, and coordination of one or many specialised agents.
Guardrails
Action allow-lists, human approval, spend and rate limits, sandboxing and a kill switch.
Observability
Full tracing of every step and decision, for debugging and audit.
Evaluation
Task-level success on a curated scenario set, with regression runs on every change.
Agentic AI compared with robotic process automation
| Dimension | RPA | AI agents |
|---|---|---|
| How it works | Fixed, scripted steps | Works from intent, reasons about the next step |
| Handles variation | Poorly — breaks on change | Yes — adapts and recovers |
| Judgement tasks | No | Yes, with guardrails |
| Maintenance | High — scripts rot | Lower — prompt and tools, not brittle flows |
| Best together | Deterministic steps | Judgement and orchestration |
A path to agents you can trust
Scope
Pick a bounded workflow; define success and the actions in play.
Prototype
A working agent with a small tool set, evaluated on real scenarios.
Harden
Guardrails, approvals, failure-mode tests and observability.
Integrate
Into your systems and identity, with least-privilege access.
Operate
Monitor success rate and intervention rate; expand scope gradually.
Agent development built for the enterprise
Agentic automation framework
Field-tested accelerators for agents across cloud, data and operations.
Safety by design
Allow-lists, approvals, limits and tracing are the default, not an add-on.
Framework-pragmatic
Native tool-use APIs and orchestration chosen for observability and maintainability.
GenAI for AIOps & SRE
Operational patterns for running autonomous systems reliably.
Platforms behind the team
RLCatalyst and Spectra accelerate build and run.
Enterprise scale
350+ Data & AI specialists across regulated industries.
Where AI agents earn their keep
Multi-step work that needs several tools and decisions rather than one answer.
Financial services
Document AI, risk and fraud models, and governed copilots that meet audit and compliance needs.
Healthcare & life sciences
Clinical and research assistants, knowledge retrieval and automation with data governance built in.
Retail & consumer
Personalization, demand forecasting, and customer and associate assistants across channels.
Manufacturing & supply chain
Predictive maintenance, quality vision, and agentic workflows for planning and operations.
Public sector
Secure, private-LLM deployments for citizen services, case work and knowledge access.
Technology & ISVs
Embed generative AI features, agents and RAG into your product with a partner who ships.
- AWS
- DataStax
- Salesforce
- Snowflake
Scaling this across the enterprise?
Our Enterprise AI Services practice brings generative AI, agents and LLMs to production at scale — with the governance, platform and operating model to keep them reliable, compliant and cost-controlled.
Agentic AI insights from Relevance Lab
Agentic AI vs Generative AI: key differences and enterprise use cases
Where generative AI ends and agentic AI begins — and which enterprise problems each is built for.
Read More BlogExploring agentic automation: transforming business operations
The framework that combines AI-driven decision-making with dynamic adaptability across business workflows.
Read More BlogFrom reactive to autonomous: GenAI's role in AIOps & SRE
Moving operations from rule-based and reactive to predictive, conversational and intent-driven with GenAI.
Read MoreAI Agent Development Services: frequently asked questions
Content last reviewed: September 2026
Have a workflow an agent could run?
Tell us the workflow and we will come back with a scoping plan, a tool list and a guardrail design.
Talk to an AI specialist
Tell us where you are on your AI journey — a use case, a proof of concept, or a platform decision — and an AI consultant will come back with next steps and a rough shape for the engagement.