Production AISystems
Knowledge and Automation

AI Agent Development

AI agents that plan, retrieve, use tools, call APIs and complete multi-step business tasks under controlled conditions. Single-agent and multi-agent systems with memory, human approval gates, retries, loop limits and full execution traces. Built for production, not for demos.

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What is AI agent development?

AI agent development is the engineering of systems where a language model does not just answer questions but takes actions—retrieving data, calling APIs, writing records and completing multi-step tasks under controlled conditions. An agent plans its approach, uses tools with defined permissions, remembers context across steps and escalates to a human when it encounters something it cannot resolve. The difference between an agent and a chatbot is that an agent acts. The difference between a demo agent and a production agent is that a production agent has retries, checkpoints, loop limits, human approval gates and a full execution trace.

Multi-step tasks are manual and repetitive

Employees collect documents, check rules, enter data and route work by hand. Agents automate these with controls.

Demo agents fail in production

No retries, no checkpoints, no loop limits. Production agents recover from failures and log every step.

Agents need access to business systems

Tools must be connected with permissions, rate limits and approval gates—not open-ended API access.

Agent behaviour is not traceable

When something goes wrong, you need to see every step, tool call and decision. Production agents log everything.

How This Solution Can Be Built

Different situations call for different implementations. These are the most common variants.

Single-agent system

One agent with a set of tools, memory and instructions. Suitable for focused, well-defined tasks.

Multi-agent system

Multiple agents with specialised roles that collaborate, hand off and review each other's work.

Human-in-the-loop agent

Agent prepares work and pauses for human approval before consequential actions. Full speed where safe, full control where it matters.

Autonomous agent with limits

Agent operates independently within strict boundaries: loop limits, tool permissions, cost caps and escalation rules.

How the System Works

From input to output, each step is engineered for a specific purpose.

1

Task received

Agent receives a business task with context and constraints

input
2

Planning

Agent breaks the task into steps and selects the right tools

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3

Information retrieval

Agent retrieves relevant data from connected systems

data
4

Tool execution

Agent calls APIs to read or write data under controlled permissions

output
5

Validation

Each tool result is validated against the expected schema and business rules

control
6

Error recovery

If a tool call fails, the agent retries with adjusted parameters or escalates

control
7

Human approval

Consequential actions pause for human review before execution

human
8

Completion and trace

Task is completed with a full execution trace logged for audit

feedback

Production and Enterprise Readiness

What makes this solution work in a real operating environment—not just in a demo.

Tool permissions

Each tool has a defined purpose, input schema and permission scope. No open-ended access.

Loop limits

Agents have configurable maximum iterations to prevent infinite loops and runaway costs.

Human approval gates

Consequential actions—payments, data writes, external communications—require human approval.

Execution trace

Every step, tool call, decision and approval is logged for audit and debugging.

Recovery

Checkpoints save state. If the agent fails, it resumes from the last checkpoint, not from the beginning.

How Success Is Measured

The right metrics depend on the solution. These are the measures that matter for this system.

Illustrative system view — metrics shown in the dashboard above are labelled examples, not client results.

Task completion

Percentage of tasks the agent completes successfully without human intervention

Tool success rate

Percentage of tool calls that succeed on the first attempt

Retry rate

How often tool calls need retries, and whether retries succeed

Human-review rate

Percentage of actions that require human approval, and whether the approval was appropriate

Cost per successful task

Total cost divided by successfully completed tasks, including inference and tool calls

View related case studies

Design an AI Agent

Tell us the workflow, problem or system you want to improve. We respond with how we would approach it.

Design an AI Agent

No finished technical specification required.

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Frequently Asked Questions

What is the difference between a single-agent and multi-agent system?
A single-agent system uses one agent with a set of tools for a focused task. A multi-agent system uses several agents with specialised roles—one retrieves, one reasons, one validates, one executes. Multi-agent systems are more capable but harder to coordinate and more expensive. The right choice depends on task complexity, cost sensitivity and the need for independent validation.
How do you prevent agents from running out of control?
Three controls: loop limits cap the maximum number of iterations, cost caps limit total spending per task, and tool permissions restrict what the agent can do. Consequential actions require human approval. If the agent hits a limit or encounters an unrecoverable error, it stops and escalates rather than continuing indefinitely.
Can agents use our internal APIs?
Yes. Tools are registered with a defined purpose, input schema, output schema and permission scope. The agent can call read APIs freely and write APIs under controlled conditions. Sensitive writes require human approval. Rate limits prevent abuse. Every tool call is logged.
What happens when an agent fails mid-task?
Checkpoints save the agent's state at each step. If a tool call fails, the agent retries with adjusted parameters. If retries fail, the agent escalates to a human with the execution trace. When the issue is resolved, the agent resumes from the last checkpoint, not from the beginning of the task.
How do you evaluate agent quality?
Task completion rate, tool success rate, retry rate, human-review rate and cost per successful task are tracked. Execution traces are reviewed against expected outcomes. Evaluation sets test known tasks against expected steps and results. Drift detection flags when agent behaviour changes over time.
When should I use an agent instead of a workflow?
Use a workflow when the steps are predictable and deterministic. Use an agent when the task requires reasoning, interpretation or adaptation—when the steps depend on what the agent finds. Many production systems combine both: deterministic workflows for known steps and agents for the parts that need judgment. Over-agentic systems are harder to run and more expensive than necessary.