The work in plain language
Propose the action. Respect the boundary.
AI agent development creates software that uses AI to interpret information and take or propose bounded steps in a business workflow. Paloren scopes the task, permissions, approvals and failure handling before building a controlled pilot. The service is primarily for mid-market organisations and smaller businesses with viable budget, access and operating ownership. An agent is not always the right approach; fixed automation may be simpler. Acceptance tests examine both useful outputs and safe stopping behaviour. Production deployment and ongoing support require explicit decisions, not an assumption of unlimited autonomy.
01 / 04AI agents
Choose an agent only when the workflow needs one
AI agent development starts by asking whether a simpler rule-based workflow can do the job. An agent may be useful when a process needs to interpret variable information and select among bounded actions. It is not a reason to automate every judgement or replace a stable integration. Paloren maps the trigger, required context, permitted tools and stopping conditions before proposing a design. An internal request-triage workflow is a useful example: interpret the request, retrieve permitted guidance and propose a route. The agent's responsibility is explicit, including what it must not decide and when the task returns to a person for resolution.
- Agent versus rule-based automation decision
- Trigger, context and bounded tool inventory
- Stopping and human-handoff conditions
02 / 04AI agents
Separate information access from action authority
An agent that can read a record should not automatically be allowed to change it. We define permissions at the task and system level, with sensitive actions requiring explicit approval where appropriate. A proposed customer update can be prepared for a reviewer without being sent. A request can be classified without granting the requested access. The design records which actor approves an action and what evidence accompanies that decision. It also distinguishes trusted instructions from untrusted material the agent encounters. External text is treated as input to inspect, not as authority to change the workflow's rules or expand its access to other systems.
- Separate read, propose and execute permissions
- Approval records for sensitive actions
- Untrusted-input handling and access boundaries
03 / 04AI agents
Test ordinary cases, exceptions and repeated events
AI agent testing needs representative tasks and clear expected behaviour, including when the correct result is to decline or escalate. We test missing information, conflicting sources, unavailable tools and instructions embedded in incoming material. Where an action can affect records, retries and duplicate events need special attention so one request does not create repeated changes. The system should expose useful execution records without assuming access to a model's private reasoning. A controlled pilot can run in a limited or proposal-only mode while users inspect the output. Acceptance criteria cover quality, action boundaries, response time and operating cost where relevant to the scoped workflow.
- Representative task and refusal test set
- Duplicate, retry and unavailable-tool scenarios
- Execution evidence for controlled pilot review
04 / 04AI agents
Plan for a person to operate and stop the agent
A production agent needs owners for the workflow, information sources and technical service. We define what is monitored, who responds to an exception and how the system is paused or reverted when necessary. Not every external action can be undone, so irreversible steps need prevention and approval rather than a vague rollback promise. Ongoing evaluation checks whether changed data or tool behaviour affects accepted performance. Support arrangements distinguish routine maintenance from new functionality. A successful pilot may justify a narrow rollout, further work or no deployment at all. The build should leave an operating system with documented limits, not an autonomous demonstration with unclear consequences.
- Workflow and technical operating owners
- Pause, recovery and irreversible-action controls
- Evaluation and support boundaries after rollout
What you take forward
A working result. And the means to keep it useful.
Workflow and alternative assessment
Permission and approval design
Scoped agent implementation
Evaluation and failure-case suite
Pilot acceptance evidence
Operating and recovery runbook
- 01
Bound the workflow
Identify the trigger, context, actions, owners and simpler alternatives.
- 02
Design authority
Separate read access, proposed actions, approvals and execution permissions.
- 03
Test the pilot
Evaluate representative tasks, unsafe requests, duplicates and tool failures.
- 04
Prepare operations
Agree monitoring, pause procedures, support and the rollout decision.
Before we begin
Your questions.
Straight answers.
How is an AI agent different from a chatbot?
A chatbot primarily organises an interaction, while an agent workflow may also use tools and manage steps toward a defined task. The categories can overlap. The important scoping questions are what information the system can access, which actions it may take and who remains responsible for them.
Can an agent run without human approval?
Some bounded low-risk steps may be approved for automatic execution, while others should remain proposal-only or require a reviewer. That decision depends on consequences and evidence, not the label agent. The scope identifies authority explicitly and tests whether the system respects those limits.
Do we need multiple agents?
Not necessarily. Multiple components can help separate responsibilities, but they also introduce coordination and testing work. We begin with the simplest design that can meet the acceptance criteria. A multi-agent architecture should solve a demonstrated requirement rather than serve as a marketing feature.
What if an external service fails?
The workflow needs an agreed response such as stopping, retrying within limits or handing off to a person with context. Tests should cover that behaviour. For actions that change records, duplicate prevention and clear execution status matter so a retry does not create unintended repeated work.
Your team. Your next chapter.