AI Agents & Systems

AI agents that do the work.

Custom AI agent development for focused workflows and personal agent runtimes—engineered to understand context, use tools, take action, and remain accountable in the real world.

See the system

TDG / SERVICE SYSTEM01 / 05

Reason · Act · VerifyAI Agents & Systems

01Agent architecture

02Context & knowledge

03Tools & action

04Evaluation & operations

System boundaries visible

The belief

Intelligence is useful only when it can cross the distance between an answer and an outcome.

What we see

The hard part is not the model.

A compelling model demo can hide everything a dependable agent still needs: trusted context, explicit tools, permission boundaries, durable state, recovery behavior, and a person who can understand what happened.

We design the complete operating system around the intelligence. The model is replaceable. The contracts, controls, memory, evaluation, and product experience are what make the agent useful after the demo ends.

What we design

01 / SYSTEM

One connected system.
Four consequential layers.

Agent architecture

Define the loop, state lifecycle, tool contracts, delegation model, and failure semantics before choosing an orchestration framework.

Runtime boundaries
State and memory
Multi-agent topology

Context & knowledge

Build retrieval and memory systems that distinguish durable truth from conversational noise and keep provenance visible.

RAG systems
Context pruning
Grounded citations

Tools & action

Give agents narrow, typed capabilities with permission checks, idempotency, sandboxing, and human approval at consequential boundaries.

Tool adapters
Execution sandbox
Approval controls

Evaluation & operations

Measure task completion, not eloquence. Trace decisions, test failure paths, route models dynamically, and make intervention straightforward.

Agent evals
Model routing
Tracing and recovery

How we work

Clarity first.
Then controlled momentum.

Frame

Choose the work worth delegating and define what success means.

Bound

Design context, permissions, tools, and human control.

Prove

Run realistic tasks against explicit evaluations and failure cases.

Operate

Ship with tracing, recovery, cost controls, and replaceable models.

Evidence

BUILT / OPERATED / LEARNED

The work informs
the way we work.

01 / Autonomous agent runtime

ARIA

A live exploration of personal agents that carry context, coordinate tools, and move multi-step work forward.

Visit the product 02 / Grounded website intelligence

Alfred

A focused agent experience that turns website knowledge into cited answers instead of unsupported conversation.

Visit the product

Related field note

AI Without the Hype

The useful question is not whether AI can be added. It is whether intelligence makes this experience clearer, faster, or meaningfully more capable.

Read the field note

Useful context

QUESTIONS / ANSWERED

The details that make
the decision clearer.

What does custom AI agent development include?

We design the operating system around an agent: its context, memory, permitted tools, approvals, evaluation, observability, and recovery behavior—not only the model interaction.

Can an AI agent automate a business workflow?

Yes, when the workflow, permissions, source data, exceptions, and human decision points are made explicit. We start with a defined job and build controlled action around it.

What is the difference between an AI agent and a chatbot?

A chatbot primarily answers. A production agent can use approved tools, work across steps, preserve relevant state, and surface its actions for review.

Have a consequential problem?

Bring us the complexity.
We’ll find the clear path.

Start with the truth of what is stuck, uncertain, or newly possible. We will begin there—not with a predetermined solution.