01
A support or intake assistant has access to a knowledge base but gives answers without grounding them in current policy, customer context, or approved sources.
AI Agents
AI agents that take bounded actions inside real business systems.
An AI agent is more than a chat interface. It uses a model alongside tools, context, and operating rules to complete a task that may require several decisions or steps. For business use, the essential design choice is what the agent can read, what it can change, when it must ask a person, and how the result is measured. Many workflows do not need an agent at all: a deterministic form-to-CRM handoff is usually better served by straightforward automation.
Evidence status
This is a research-backed technical capability overview. The portfolio does not yet include a publicly documented project built specifically with AI Agents. The evidence linked below demonstrates related engineering patterns, not a completed client implementation on this exact platform.
When this helps
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An agent can draft a useful action but should not send a message, change a record, book an appointment, or move money without the right permissions or approval rules.
03
Teams do not know when the agent is wrong because they have no representative test cases, failure categories, traces, or escalation process.
04
The cost and latency of a multi-step agent become unpredictable because tools are called repeatedly or the model is asked to resolve tasks that fixed logic could handle.
What the work involves
01
Define one narrow job and success criteria first: for example, classify a lead, retrieve CRM context, suggest the next action, and ask a person to approve an outward-facing reply.
02
Design tools with explicit input/output schemas, role-based access, limited credentials, and validation before state-changing actions. Keep sensitive data to the minimum needed.
03
Ground responses in the right documents or systems of record. Choose retrieval, deterministic rules, or multi-step reasoning only where the task benefits.
04
Build a representative evaluation set including edge cases, conflicting records, bad inputs, and tool failures. Record traces and have a clear human fallback.
How I approach delivery
Discovery identifies the decision the agent makes, available data, business risk, and existing workflow. The first version should prove a narrow end-to-end action with clear stop conditions.
The implementation connects tools and context, defines approval points, tests against known cases, and records failure reasons. A pilot is reviewed against response quality, latency, cost per resolved task, and escalation rate.
Production operation needs monitoring, versioned instructions, data-access controls, and a process for correcting errors. Complex multi-agent designs are considered only when a simpler single-agent workflow is insufficient.
Scope and important limits
I would not describe a workflow as agentic merely because it includes an AI text-generation step. Agents should be selected for tasks that require contextual decisions or tool choice.
Existing portfolio examples demonstrate CRM, workflow, and application integration foundations. They are linked as related engineering evidence, not as invented performance claims for deployed autonomous agents.
Related work
These are existing projects relevant to the problem space. They are not all examples built on this specific platform.
Lead enrichment workflow
Enrichment, contextual data, and outreach handoff
CRM operating architecture
System boundaries, ownership, and follow-up processes
AI applications
Examples of application-oriented development work
Common questions
What is the difference between an AI agent and normal automation?
A normal workflow follows explicit conditions and actions. An agent can use a model to interpret a task, select a permitted tool, and decide between bounded next steps. For predictable routing rules, ordinary automation is often simpler, cheaper, and more dependable.
Can an AI agent safely update a CRM?
It can be designed to do so, but credentials, field validation, duplicate handling, authorization, audit logging, and approval thresholds must be specified. Not every action should run without human confirmation.
How do you measure agent quality?
Use realistic task-level evaluations rather than demo transcripts alone: accuracy, completion rate, appropriate escalations, failed tool calls, time to resolution, and cost. Review failures as the system and business processes change.
Technical references
Primary documentation used to verify the platform capabilities discussed above. Features and access can change.
OpenAI: building agents and tool workflows
Explore related work