What makes an application agentic?
A conventional AI feature often responds once to a prompt. An agentic workflow can decide which approved step to take next: search a knowledge source, call a business API, check a result, ask for missing information or prepare a proposed action. The application—not the model alone—defines which tools exist, what information they can access, how steps are validated and when a person must approve an outcome.
Good agentic design is selective. A deterministic form, rule or integration is usually preferable when a task has a fixed path. Agents are most useful when work involves ambiguity, unstructured information or a sequence of decisions that benefits from contextual reasoning.
Where agentic applications can help
Operations copilots
Correlate alerts and runbooks, summarize likely causes, gather diagnostics and prepare a remediation for an engineer to review.
Knowledge assistants
Answer questions from approved internal documents with retrieved evidence, source references and access-aware results.
Service workflow assistants
Classify requests, retrieve relevant account context, draft responses and route cases to the correct team under defined rules.
Developer productivity
Help explain code and infrastructure, propose tested changes or open a review request without bypassing existing approvals.
Core architecture and tools
A production system typically combines a model endpoint, a workflow orchestrator, approved tools, identity-aware data retrieval, policy checks and application telemetry. Depending on the requirements, orchestration frameworks may include LangGraph, LangChain or Microsoft Semantic Kernel; model services may include managed offerings from AWS, Google Cloud, Microsoft or other providers. Selection depends on data location, latency, quality, cost, operational fit and portability requirements.
| Layer | Purpose | Key design question |
|---|---|---|
| Model | Interpret instructions and produce structured decisions | Which model meets quality, latency and data requirements? |
| Orchestration | Manage steps, state, retries and handoffs | Can each transition be inspected, bounded and resumed? |
| Tools | Read data or request a defined action | Are permissions narrow, validated and scoped per user? |
| Knowledge | Provide relevant, authorized reference material | Are sources current and access controls preserved at retrieval? |
| Evaluation | Measure quality, safety and operational outcomes | Are changes tested against representative cases before release? |
Security and human oversight by design
Agentic applications can amplify mistakes if tools are broad or execution is opaque. Separate read-only capabilities from change-making tools, authenticate tools with workload identities, scope access to the current user's permissions and validate every argument against server-side rules. Treat retrieved content as untrusted input; it must not override system policy or grant new permissions.
- Require explicit approval before consequential, external or irreversible actions.
- Use least-privilege credentials, short-lived access and environment separation.
- Set limits for steps, time, cost, retries and tool-call frequency.
- Validate outputs with schemas and deterministic business rules.
- Log model, prompt/template version, retrieved sources, tool calls and approvals with sensitive data minimized.
- Provide a safe fallback, escalation path and kill switch.
From prototype to dependable product
- Choose a bounded workflow: define the user, task, allowed data and actions, and what success looks like.
- Map the risks: classify actions by impact and decide where approval, stronger validation or a deterministic path is required.
- Build a constrained prototype: add only necessary tools and context, then test representative normal and failure cases.
- Evaluate continuously: track task completion, factual grounding, tool selection, policy violations, latency and cost across a curated test set.
- Deploy progressively: release to a limited group, review traces and feedback, and expand only when evidence supports it.
- Operate and improve: monitor drift, model/provider changes, data quality, permissions and incident response readiness.
Measure business outcomes
Measure whether users complete the task more accurately or quickly, how often the agent needs correction, how frequently it escalates appropriately, and the cost and latency per successful outcome. Track unsafe tool attempts and permission denials as safety signals. Avoid treating message volume or autonomous action count as evidence of value on its own.
Frequently asked questions
Does an agent need permission to make production changes?
Only if the use case explicitly calls for it. Start with read-only investigation or draft-and-review workflows; introduce carefully scoped execution only after testing and approval controls are established.
Can agentic applications use our existing cloud and identity systems?
Yes. The design can integrate with existing cloud services, identity providers, APIs and monitoring while respecting data access boundaries and operational policies.
How do you decide whether to use one agent or several?
Start with the simplest orchestration that meets the task. Multiple specialized agents add coordination, security and evaluation complexity, so they should be used only when clear boundaries materially improve the outcome.
Plan an agentic application
Bring a workflow challenge and we can help assess whether an agent is appropriate, define safe boundaries and shape a production-ready first release.
Discuss your use case