AI, Data & Intelligence
Agentic AI Systems
Multi-agent and long-running AI workflows that plan, delegate and coordinate across several systems and teams, with orchestration, shared state, checkpoints and governance designed for production.
Capability overview
What agentic ai systems involves
Some processes are too broad for a single agent: a supplier onboarding flow that checks documents, verifies registrations, assesses risk and sets up records in several systems, or an incident workflow that gathers diagnostics, proposes a fix and coordinates approvals. Agentic systems break such work into specialised agents coordinated by an orchestrator.
Reliability comes from structure rather than autonomy. We model workflows as explicit graphs with durable state using frameworks such as LangGraph or workflow engines like Temporal, define which agent owns each step, and place human checkpoints at decision points. Guidance such as the OWASP Top 10 for LLM Applications informs the security design.
What is included
Components of an agentic system
Orchestration layer
A controller that plans work, routes steps to specialised agents and handles retries, timeouts and escalation when an agent fails.
Durable state and memory
Workflow state persisted outside the model so long-running processes survive restarts and can resume after human input.
Specialist agents
Agents with distinct roles, tools and permissions, such as a document checker, a research agent and a system update agent.
Governance controls
Policies for which actions need approval, spending and rate limits per workflow, and kill switches to halt a run.
End-to-end tracing
Traces across agents and tools showing why each decision was made, essential for audit, debugging and improvement.
How we work
How we deliver agentic ai systems
Process decomposition
The target process mapped into steps, decisions, data and system touchpoints with the teams who own it today.
Autonomy design
Each step classified as automated, AI-assisted with approval or human-only, based on risk and regulatory requirements.
Incremental build
Agents built and tested one at a time, then connected through the orchestrator in a staging environment.
Scenario testing
Hundreds of synthetic and historical cases run end to end, including failures in downstream systems.
Phased go-live
Workflows launched for a subset of cases with close monitoring, expanding coverage as performance is proven.
Related capabilities
Related capabilities in Artificial Intelligence
Conversational AI
Customer-facing chat and voice assistants on your website, app, WhatsApp and phone lines that resolve common requests, speak your customers' languages and hand over to people with full context.
Enterprise AI Integration
Connecting AI capabilities into the systems your organisation already runs, such as ERP, CRM, ITSM, document management and collaboration tools, with identity, permissions and audit carried through.
AI Model Integration
Wiring trained machine learning models and model APIs into existing software, from real-time scoring endpoints and batch predictions to model versioning, fallbacks and safe upgrades.
AI Deployment
Taking an AI system from a successful pilot to dependable production: hosting, scaling, security review, monitoring, rollback and the operational handover that pilots usually skip.
Explore further
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Where this applies
Healthcare & Life Sciences
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Questions & answers
Questions about Agentic AI Systems
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionNot automatically. More agents mean more coordination overhead and failure points. We use multiple agents only when roles, permissions or tools genuinely differ, and keep simpler designs when a single agent is enough.
Common choices include LangGraph, Microsoft Agent Framework, the OpenAI Agents SDK and durable workflow engines such as Temporal. Selection depends on your language stack, hosting and how much control the workflow requires.
Each workflow has budgets for tokens and tool calls, smaller models handle routine steps, results are cached where possible, and dashboards show cost per completed case.
A design phase is fixed price and produces a detailed estimate. The build is delivered in fixed-scope phases, one workflow segment at a time, with operation available as a managed service.
Next step
Discuss agentic ai systems with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.