Inputs
List the systems, files and public sources the agent may read. Define how fresh the information must be and what happens when a source is unavailable.
Plan a controlled prospect list enrichment AI agent for electrical contractors: scope, tools, approvals, metrics and implementation steps using customer-owned models.
A controlled agent is best treated as a specialist operating role with written instructions, limited tools and review checkpoints. For electrical contractors, this matters in project, property, contractor, tenant and documentation workflows with many handoffs and deadlines. A prospect list enrichment role can standardize approved prospect records and fill permitted public-data gaps. The target output is an enriched prospect table, not an opaque autonomous decision.
The operating boundary should be written before tools are connected. Specify the allowed sources, required fields, freshness expectations, acceptance criteria and the person who owns exceptions. Separate retrieval, analysis, drafting and action so each step can have a different permission level and reviewer. This makes the same workflow easier to test, audit and improve because failures can be traced to a source, instruction, permission or model behavior.
Adapt the details to your systems and policies; keep each stage observable.
1. Intake. Accept the defined request, file, record or schedule event and verify that required context is present.
2. Retrieve. Use only permitted sources needed to standardize approved prospect records and fill permitted public-data gaps. Record citations, record IDs or source references where the workflow allows it.
3. Prepare. Produce an enriched prospect table using the required structure, terminology and completeness checks for electrical contractors.
4. Review. Route ambiguous, sensitive or high-impact cases to the named human owner. Keep contractual, safety, property-access and financial commitments under qualified human review.
5. Measure. Track field completeness, duplicate rate and reviewer corrections; compare against the manual baseline and investigate exceptions before expanding permissions.
List the systems, files and public sources the agent may read. Define how fresh the information must be and what happens when a source is unavailable.
Define the schema for an enriched prospect table: mandatory fields, citations, confidence notes, unresolved questions and the next action proposed.
Give read access before write access. Keep external communication, account changes and irreversible actions behind appropriate approvals.
Test representative normal cases plus missing data, conflicting instructions, stale sources, duplicates, tool failures and requests outside scope.
Name the person responsible for this workflow, exception handling and periodic review. Automation without ownership usually creates hidden operational debt.
Compare total cost per accepted output, including model/API usage, paid tools, human review, retries and setup—not only the listed agent hourly rate.
Build a small benchmark from real, permission-safe examples of prospect list enrichment work in electrical contractors. Run the same examples through the manual process and the proposed agent-assisted process. Measure field completeness, duplicate rate and reviewer corrections. Add human correction rate, exception rate, latency and total cost per accepted output so a faster workflow is not mistaken for a better one when quality falls.
Keep a holdout set for later changes to instructions, tools or models. A change should be promoted only if it improves the outcomes that matter without creating unacceptable safety, compliance or customer-experience regressions. This turns model selection into an operating decision based on evidence rather than a one-time product preference.
It can standardize approved prospect records and fill permitted public-data gaps and prepare an enriched prospect table from permitted information. The exact capability depends on the model, connected tools, source quality and permissions configured by the customer.
Not by default. Keep contractual, safety, property-access and financial commitments under qualified human review. Start with reviewable outputs and expand authority only after measured testing.
Track field completeness, duplicate rate and reviewer corrections. Also monitor exception rate, human correction rate, time saved and any policy or data-quality issues.
The platform is designed around customer-owned supported model credentials. Model/API usage is billed separately by the selected provider, while RentAgents provides the agent workspace and marketplace layer.
Start with reviewable outputs, customer-owned model credentials and only the tools the role actually needs.