AI Agents & Internal Assistants
An agent is only useful if it survives contact with production. I build tool-using AI agents for support triage, research, document processing, and internal operations — with eval suites, guardrails, and human handoff designed in from day one.
This fits if…
- Businesses with high-volume, judgment-light work drowning a team
- Teams that want AI working inside operations, not in a demo tab
- Companies burned by an AI pilot that never reached production
What you get
- Agent architecture: model selection, tools, memory, and state
- Tool and MCP integration with your real systems and data
- Eval harnesses so behavior is measured before and after every change
- Guardrails and confidence thresholds with human-in-the-loop handoff
- Observability: logs, traces, and cost tracking per run
Sound familiar?
Tier-1 support tickets that follow the same five patterns all day
Account research a rep does by hand before every outbound touch
Documents that need reading, extracting, and reconciling at volume
Internal questions that pull an operator away from real work
Related case studies
A tier-1 triage system that classifies, drafts, and routes support tickets — so humans approve instead of typing.
A repo-aware coding agent that plans, edits, and opens pull requests with passing tests — no hand-holding chat sessions.
Daily company briefs — sourced, scored, and filed in Notion before the team sits down — replacing ad-hoc analyst scrambles.
Common questions
How is an agent different from a normal automation?
An automation follows a fixed path; an agent decides between paths using tools and context. The rule of thumb: automate what is deterministic, use an agent where inputs vary and judgment is cheap to verify.
What stops an agent from making things up?
Constrained tools, retrieval over your own data, confidence thresholds, and evals that catch regressions. Below-threshold cases route to a human — the agent drafts, a person approves.
Which models do you build on?
Claude primarily, with GPT, Gemini, or self-hosted Llama where cost or constraints call for it. Architecture stays model-portable so you’re not locked in.
More in the full FAQ — pricing, timelines, and how engagements run.
Related insights
The words get used interchangeably in sales calls, and the confusion causes real project failures. The distinction is simple: automations follow paths, agents choose them.
Every company has a graveyard of impressive AI demos. The pattern of failure is consistent, and so is the pattern of the survivors.
Discuss this service
Book a call and we'll scope whether ai agents & internal assistants is the right first move for your operations — and what it would take.
30 minutes · no pitch deck · reply within 24h if you write instead