Custom AI Agents

A custom AI agent is justified when your process has specific steps, data sources or approval requirements that a configurable product cannot handle without significant compromise. Paloren assesses this during discovery by mapping the workflow end to end. If an existing tool covers 80 percent of the need and the remaining 20 percent can be handled manually, a custom build may not be worth the cost. If the process involves proprietary data, regulatory boundaries or a multi-system handoff that no product supports, a custom agent built against your systems is the right approach. Paloren recommends the smallest build that solves the problem, not the largest one that can be sold.

• Workflow exceeds standard tool capability
• Proprietary or regulated data involved
• Multi-system handoff not supported elsewhere

Every custom agent Paloren builds includes the same structural elements. Context: which systems provide the information the agent reads. Actions: what the agent can do, from drafting to updating records. Boundaries: which actions require a person to accept, and what the agent must never access. Testing: representative cases covering correctness, permissions, missing data and recovery. Ownership: the named person who accepts the output and the support model after launch. The proposal documents all five elements before build starts, so the scope is clear and comparable against other proposals.

• Context and action definitions
• Approval and access boundaries
• Test cases and acceptance criteria

Scoping starts with discovery interviews with the people who do the work. Paloren identifies the process steps, the systems involved, the data quality and the decision points. Source contracts follow: which fields are read, who owns each source and what happens when a feed fails. Integration design maps how the agent connects to each system. The test plan defines representative tasks, edge cases and acceptance criteria. This sequence prevents misdirected builds and surfaces dependencies, such as API access that needs approving, before they become blockers during delivery.

• Discovery with the people who do the work
• Source contracts and field ownership
• Test plan agreed before build

Custom agents that touch sensitive data need explicit controls. Paloren designs permission-aware retrieval so users only see records their role allows. The agent documents which fields it reads and writes. Evidence records attach the source of every answer so a reviewer can verify the basis for a recommendation. For regulated industries, Paloren works alongside your compliance team to map controls to existing requirements. Legal and regulatory interpretation remains with qualified advisers, but the technical controls that make those rules enforceable are part of the build.

• Permission-aware retrieval
• Source evidence attached to answers
• Controls mapped with compliance team

A configured tool starts with someone else’s data model and workflow assumptions. You adapt your process to fit. A custom agent starts with your process and builds the connection layer to match. Paloren builds custom agents when the gap between the tool’s assumptions and your workflow creates risk or manual work. For example, if your approval chain spans three systems and a configured tool only supports one, the manual bridge is where errors happen. A custom agent removes that gap by design. The trade-off is that custom builds need more upfront scoping and a longer timeline than configuring a product.

• Configured: adapt process to the tool
• Custom: build tool around the process
• Custom needs more upfront scoping

Custom agents need testing that goes beyond a demo. Paloren runs cases covering correctness, isolation, recovery and evidence. Correctness checks that the output matches the source. Isolation confirms that a user only sees what their permissions allow. Recovery tests what happens when a source is unavailable or returns an error. Evidence verification confirms that the answer carries its sources. All required cases must pass before go-live. Changes to sources or permissions trigger re-testing, so the standard is maintained rather than assumed. This is the same testing framework Paloren applies to every production system, whether custom or configured.

• Correctness against source data
• Isolation and permission testing
• Recovery and evidence verification

Custom agent workflows run USD 40k to 90k over 6 to 10 weeks. The cost is driven by the number of connected systems, the complexity of the permission model, whether the agent takes actions or only prepares recommendations, and the depth of testing required. A custom agent that reads two systems and drafts a summary costs less than one that moves records across four platforms with approval gates. The proposal names deliverables, assumptions, exclusions and acceptance criteria. Ongoing support is scoped separately, typically from from USD 2,500 per month for 10 hours.

• Scoped by systems and permissions
• Testing depth affects cost
• Support priced separately

The handover pack includes system documentation, evaluation evidence, training materials, the operating model and a named owner. Documentation should be complete enough that a new technical person could understand the system without the original team. Paloren prepares this as part of delivery rather than as an afterthought. Training prepares the people who will use the agent so adoption does not depend on the build team remaining available. The runbook describes what to do when something fails, so the team has a process rather than a phone number.

• System documentation and evaluation evidence
• Training for the people who will use it
• Runbook and operating model included