AI agents and workflow automation

A support request can require information from several systems, an interpretation of policy and an action the company must stand behind. A document review can require the right versions and attachments, a judgment against business requirements and a result another person can use. Automating either starts with understanding the whole process.

I work with the people responsible for that process to decide which steps belong in ordinary software, which benefit from a model and where an agent needs flexibility to investigate. The design connects those choices to integrations, human decisions and the result the business needs.

Written procedures are a starting point. I also examine completed work, exceptions, corrections and reopened cases, and talk with operators, managers and the people receiving the output. Their experience reveals decisions and dependencies that documentation often leaves implicit.

We turn that understanding into requirements: what information governs, which actions are permitted, when a person must decide and what demonstrates completion. I then develop the application and integrations with your engineers or take responsibility for an agreed implementation. The workflow-redesign article examines how tasks, exceptions and continuing responsibility change together.

Extracting fields and tables, answering questions over a collection and reviewing a complete document packet are different tasks. The design follows what the person needs to do with the result. An agreement review, for example, needs to establish which versions and attachments govern before assessing its requirements.

My delivery experience includes a production contract-intelligence system connecting documents to structured interpretation and business rules. In a new system, I establish source authority, link findings to evidence and make correction part of the workflow. The retrieval and access resource develops the choices around search, source currency and permissions.

Parsing supports that larger system. PDF-Bench explains how to compare extraction results against the fields, tables and references the task needs. PDFsmith provides a common interface for alternative parsers.

A procurement analyst needs an NDA assessed against company requirements. The Contract Review demonstration follows the investigation from the agreement, competing amendment copies and an internal policy to an exception memo. The result identifies a backup-retention exception and a missing security exhibit, with evidence for each finding.

Changing the proposed use of customer data changes what must be investigated and which decisions go to Legal or Security. The decision-record resource explains how to connect a finding to its evidence, authorized actions and responsible owner.

The Customer Support demonstration investigates a replacement that never arrived after the original return window closed. The agent reads order and shipment records, applies the remedy policy, requests an exception decision and confirms the customer's choice before completing the refund in the demonstration environment.

The model chooses what to examine and ask next; application code enforces ownership, approval and the amount that can be refunded. Variations show a customer declining the refund and a response being lost after the refund was recorded. They illustrate why flexible investigation needs reliable control of system actions. The article on AI decision models examines the relationship between computational judgments and the policy that authorizes a decision.

I develop representative cases, acceptance criteria and evaluation tools alongside the application. For document review, I check coverage and whether findings follow from the sources. For a system taking actions, I examine the resulting records, policy compliance and recovery from interruptions. Unresolved cases and the effort spent reviewing and correcting results remain part of the assessment.

The review arrangement follows the consequences of error and measured performance. Some decisions need prior approval; others can use exception handling and sample-based checks. The workflow-evaluation article follows a complete example, while the benchmark article explains what external scores can establish about a deployment choice.

For the test environment itself, the business-agent environment resource compares the kinds of records, actions and consequences an evaluation needs to represent.

An ordinary application, a fixed workflow with model-assisted steps and an agent should be compared on the same workload and quality requirements. I include implementation, processing, review, correction, exceptions and maintenance. A faster first answer can create more checking work elsewhere.

The whole-workflow economics article shows how saved time differs from usable capacity and cash savings. The operating-cost resource supports a more detailed comparison. Better source data, a direct integration or a clearer handoff can improve the process more than another model call.

Acceptance is followed by continuing operation. Documents, policies, tools and workloads change; monitoring needs to reveal when those changes affect results. I help define who investigates exceptions, owns corrections and decides when evidence must be renewed.

The operations and reevaluation guide sets out that process. Reviewing difficult and reopened cases also reveals missing requirements and expert knowledge. The article on organizational knowledge and learning examines how those findings can improve both the system and the organization's understanding of its work.

My financial-markets work includes quantitative research and live trading platforms used in production over multiple years, with execution, risk controls and operating responsibilities. That experience informs how I approach integrations, persistent state, monitoring and recovery in business applications.

In Machine Learning for Trading, the implementation treatment extends to research and forecasting agents, retrieval over financial documents, and evaluation. The accompanying Agent Lab presents dated forecasts and reasoning from an operating forecasting system.

The application itself needs a defined task, a working test environment and review of the resulting change. The Coding Agent Toolkit supports specification, implementation, verification and continuation across sessions.

Using coding agents: from a written task to a tested change follows a constructed development exercise through an initial defect, independent tests and a corrected patch. It complements the business cases by showing how the software behind a workflow is checked.