NeoTek Solutions designs, builds and runs agentic AI architecture for clients who need software that completes work, not just answers questions. Agentic AI is a design where a large language model (LLM) plans steps and calls tools to finish a task. An AI agent can look up records, draft updates and trigger workflows within limits you set. Our Nashville team adds the permissions, approvals and audit logs that keep those actions safe.
When to Use This Architecture
Agents fit multi-step work that needs judgment across several systems. Examples include triaging support tickets, preparing a vendor onboarding packet, reconciling exceptions or researching a customer account before a call. They also fit tasks where the exact steps vary from case to case.
A simpler option is often better:
- If the steps are fixed and predictable, a standard workflow or robotic process automation is cheaper and easier to test.
- If users only need answers from documents, use retrieval-augmented generation instead.
- If a mistake would be costly and hard to reverse, start with an agent that drafts actions for a person to approve.
For ideas on where to begin, read five practical first AI agent projects.
Core Components
Agent Runtime and Model
We build the runtime that runs the agent loop: read the goal, decide the next step, call a tool, observe the result and repeat. The LLM does the reasoning inside that loop. We set step limits, timeouts and stop conditions so an agent cannot run forever.
Single Agent or Multi-Agent Orchestration
We start most clients with a single agent and a small set of tools, because it is easiest to test. Multi-agent orchestration splits work across specialist agents, such as a research agent and a writing agent. We use it only when tasks are large or need clearly separate permissions.
Planner and Executor
We often split the design so one step produces a plan and another carries out each step. That lets us show the plan to your staff before anything runs. The separation also makes behavior easier for us to review, debug and control.
Tools and Tool Calling
We define the tools an agent may call, such as “search orders” or “create ticket.” The model returns a structured request naming the tool and its inputs. Your code runs the tool, checks the inputs and returns the result to the model.
Model Context Protocol (MCP)
We connect agents through the Model Context Protocol (MCP), an open standard for linking AI applications to tools and data sources. An MCP server exposes a system’s capabilities in a consistent way, so your other agents can reuse it. We still apply authentication, least privilege and logging to every MCP connection.
Memory and State
We track short-term state for the current task, plan and tool results. For long-term memory we store approved facts, such as user preferences or past case notes, in a database. We save state so a paused task can resume after an approval or a failure.
Human-in-the-Loop Approvals
We put your people in the loop, so a person reviews and approves certain actions before they run. We set approval rules by risk, such as sending external email, changing financial records or deleting data. Your reviewer sees the proposed action, the reasoning and the supporting data.
Audit Logging
We record every goal, plan, tool call, input, output and approval with a timestamp and identity. Those logs support your troubleshooting, compliance review and evaluation. They also show you exactly what the agent did and why.
How It Works
- A user or system event starts a task, such as a new ticket arriving.
- The runtime loads the goal, relevant context and the tools this agent may use.
- The model proposes a plan or the next step.
- The runtime validates each proposed tool call against the allowed tools and input rules.
- Low-risk calls run in a sandbox with scoped credentials, and the result returns to the model.
- High-risk actions pause and go to a person for approval.
- The loop repeats until the task is complete, a limit is reached or the agent escalates.
- The agent reports the outcome, and every step is written to the audit log.
- Evaluation jobs review logged runs to improve prompts, tools and rules.
Security, Governance and Guardrails
Least privilege. Each agent gets its own identity with only the permissions its task needs. Read access and write access are separated. Credentials are stored in a secrets manager, never in prompts.
Sandboxing. Tools that run code or touch files execute in isolated containers with limited network access. This contains the damage if a tool is misused.
Prompt injection defenses. Prompt injection is hidden text in emails, web pages or documents that tries to redirect the agent. We treat tool results as untrusted, restrict which tools can follow untrusted input and require approval for sensitive actions.
Data protection. Sensitive data is masked where the task does not need it. Model calls can pass through an LLM gateway for redaction, filtering and cost limits.
Evaluation and monitoring. We test agents against scripted scenarios, including edge cases and attack attempts, before release. In production, we monitor task success, escalations, tool errors and cost.
Human oversight. People own the outcome. Agents escalate when confidence is low, data is missing or a rule blocks an action.
Reference Stack by Cloud
| Component | Microsoft Azure | AWS | Google Cloud |
|---|---|---|---|
| LLM access | Azure OpenAI | Amazon Bedrock | Vertex AI |
| Agent runtime | Azure Container Apps or Azure Functions | Amazon Bedrock Agents or AWS Lambda | Cloud Run |
| Workflow and approvals | Azure Logic Apps | AWS Step Functions | Workflows |
| State and memory | Azure Cosmos DB | Amazon DynamoDB | Firestore |
| Secrets | Azure Key Vault | AWS Secrets Manager | Secret Manager |
| Identity | Microsoft Entra ID | AWS IAM | Cloud IAM |
| Audit and monitoring | Azure Monitor | AWS CloudTrail and Amazon CloudWatch | Cloud Logging |
Open-source and on-premises options also fit, including open agent frameworks, MCP servers, PostgreSQL and Kubernetes. NeoTek Solutions is vendor-neutral and picks components based on your systems and requirements.
Common Pitfalls
- Building a multi-agent system when one agent or a fixed workflow would do.
- Giving an agent broad administrator credentials instead of scoped permissions.
- Letting agents take irreversible actions without an approval step.
- Trusting content from emails or web pages as if it were instructions.
- Skipping step limits, which can create loops and runaway cost.
- Logging only final answers instead of every tool call and decision.
- Launching without scenario tests and a clear escalation path.
Where We Apply It
In logistics and supply chain, agents can investigate shipment exceptions and draft carrier or customer updates. In healthcare, they can prepare prior authorization packets or intake summaries for staff review. In financial services and insurance, they can gather documents for claims or account reviews. In manufacturing, they can compile supplier quality information and open follow-up tasks.
Example scenario: An operations team spends hours researching order exceptions across three systems. An agent gathers the facts, proposes a resolution and waits for a coordinator to approve it.
How Can NeoTek Solutions Help You Build AI Agents?
You do not have to work out the tools, limits and approvals on your own. We deliver the whole agent, or we join your team for the parts you want help with.
- What we buildSingle agents and multi-agent workflows with scoped tools, step limits, approval rules, sandboxing and full audit logging, built around a task you already repeat.
- Built on your systemsWe connect agents to your records, ticketing and line-of-business systems through secure APIs and MCP servers, with least-privilege credentials for each one.
- How we workShort cycles with AI-assisted delivery and human review, scenario tests agreed early and a working agent on your own data, so scope and cost stay visible.
- Skills on the teamAI and machine learning engineers, data engineers, cloud and security specialists and QA in one team, so tools, permissions, sandboxing and testing are all covered.
- What makes us differentOne team covers strategy, build and staffing, so we can hand the agent over to your people or stay on alongside them.
- People stay in chargeApproval rules follow your risk appetite, agents escalate when confidence is low and your data is never used to train public models.
Tell us about one multi-step task your team repeats every day. Book a free AI consultation and we will walk through the tools, limits and approvals it would need.
Frequently Asked Questions
What do your agents do that a chatbot cannot?
We build agents that plan and carry out multi-step tasks by calling your tools and systems, not just answering questions. One of ours can look up records, draft updates and trigger workflows. It works within the permissions, approvals and audit logging we set up with you.
Do you use the Model Context Protocol (MCP)?
Yes, where it helps. MCP is an open standard for connecting AI applications to external tools and data, and it gives your agents a consistent way to call capabilities. We still apply authentication, least-privilege access and logging to every MCP connection we build.
Would you build us one agent or several?
We start with a single agent and a small set of tools, because it is easier to test, secure and explain. We move to multi-agent orchestration only when the task is large, needs specialist roles or requires separate permissions.
How do you stop an agent taking harmful actions?
We give each agent scoped credentials, run risky tools in sandboxes and require approval for high-impact actions. We log every step. We run scenario tests before launch, including attack attempts, and monitor task success and escalations afterward.
Where do our people approve what the agent does?
We agree the approval rules with you, usually by risk, then build them into the agent’s path. Before a flagged action runs, your reviewer sees the proposed action, the reasoning and the data behind it. Your people own the outcome.
Design Your First AI Agent Safely
Tell us about a multi-step task your team repeats every day. We will help you design an agent with the right tools, limits and approvals. Learn more about AI agents and intelligent automation or talk to an AI architect.