AWS Document Agent
Autonomous document pipeline for four regulatory document classes with orchestration, approvals, and guardrails.
Removed 95% of manual effort for dynamic document creation.
Read case study →AI Agents
AI agents for internal workflows that need real operational reliability
I design and ship AI agents for teams that need more than a demo: internal document agents, workflow agents, and human-in-the-loop systems that reduce manual effort without losing control, auditability, or delivery speed.
Who this is for
Operations, legal, compliance, and delivery teams that handle repetitive knowledge work, approvals, document generation, or multi-step internal workflows.
What problem this solves
Most teams know where AI agents could save time, but the hard part is making them reliable: defining tool boundaries, building review steps, handling failures, and avoiding brittle prompt chains that collapse outside a happy path.
What I build
I build agents that combine orchestration, retrieval, validation, and review steps so they can operate inside real production workflows rather than isolated chat demos.
- Document agents for structured pack generation and approvals
- Workflow agents that call tools, APIs, and internal systems with guardrails
- Human-in-the-loop patterns for regulated or high-risk steps
- Prompt, retrieval, and observability layers for production support
Relevant proof
The strongest proof for this service is delivery work where automation had to be trustworthy, measurable, and usable by non-ML stakeholders.
IMM Risk Workflow Automation
Production tooling for risk workflows that reduced manual batch execution and improved operational reliability.
Reduced manual intervention across IMM batch workflows in a production risk context.
Read case study →When to use this
- You have a repetitive internal workflow with clear steps and review points.
- The process depends on documents, structured inputs, or tool calls across systems.
- You need automation with traceability, approval checkpoints, or operational handoff.
- You want an agent that plugs into a business workflow, not a generic chat surface.
When not to use this
- You only need a deterministic rules engine or a cron-based script.
- The workflow has no stable objective, no measurable outcome, or no defined owner.
- There is no appetite for review steps, observability, or failure handling in production.
Engagement shape
Discovery / design
Map the workflow, define tool boundaries, decide where the agent should stop, and set review criteria.
Build / implementation
Ship orchestration, prompting, retrieval, approvals, and integrations around the workflow that matters.
Hardening / productionization
Add monitoring, retry logic, fallback handling, and operational visibility so the agent can survive real usage.
FAQ
What kind of AI agents do you build?
I focus on internal workflow agents, document agents, and tool-using agents that support real teams rather than consumer chat products.
Do these agents require a human reviewer?
When the workflow is regulated, high-risk, or client-facing, I usually recommend explicit review checkpoints instead of full autonomy.
Can you work with AWS, Azure, or existing enterprise systems?
Yes. The implementation depends on the stack already in use, but I typically work across cloud platforms, APIs, queues, and internal operational systems.
How do you keep an agent reliable in production?
By constraining scope, defining tool boundaries, adding approval steps where needed, and instrumenting the workflow so failures are visible and recoverable.