Agentic consultancy
Build AI workflows that know when to think, when to act, and when to ask.
I help teams understand and build agentic systems: model harnesses, memory patterns, skill libraries, tool access, API workflows, and practical operator habits. No black box theatre. Just clear systems that can be taught, tested, and improved.
How agentic workflows actually work
The model is only one part of the machine.
The useful work happens around it: the harness, the memory, the tools, the approvals, the scripts, and the habits that make the workflow reliable.
Model harnesses
A harness wraps the model with rules, tools, memory, files, prompts, safety rails, and repeatable operating patterns, so the AI behaves like part of a system rather than a loose chat window.
Memory layers
Good agent work separates short-term task context from durable project memory, preference memory, documents, examples, and decision logs, so the agent knows what to remember and what to forget.
Skills and playbooks
Skills turn repeated judgement into reusable routines: how to research, how to write, how to inspect code, how to use APIs, and how to hand work back cleanly.
Tool and API access
MCPs, backend functions, APIs, connectors, filesystems, browsers, terminals, and databases let agents act with permission instead of only generating text.
Implementation signals
Calm on the surface. Complex underneath.
The goal is not to show off the machinery. It is to set it up properly, document it clearly, and teach people how to operate it with confidence.
Prompt architecture, routing, critique loops, and escalation rules
VPS setup, shell habits, PowerShell workflows, logs, and process control
Codex-style coding agents, Claude-style coworking, and supervised build loops
MCP servers, API keys, OAuth boundaries, webhooks, and local tool bridges
Workspace memory, skill libraries, reusable instructions, and operator checklists
Human approval gates, rollback thinking, testing passes, and audit trails
Consultancy shape
I can build it with you, or teach your team how to build it themselves.
01
Frame the job
Define the business goal, source material, success criteria, risk level, and who approves the output.
02
Choose the harness
Decide whether the work needs a simple assistant, a coding coworker, a research chain, or a tool-using agent.
03
Wire the tools
Connect safe access to files, APIs, MCPs, dashboards, scripts, terminals, and data sources only where needed.
04
Teach the loop
Create skills, examples, memory notes, review prompts, and recovery steps so the workflow improves instead of drifting.
Private by design
The public lesson is the method. The private work stays private.
I can explain the principles, design the workflow, and train operators without exposing proprietary dashboards, private experiments, or internal agent architecture.