AI agents with tools, limits, and an audit trail
Task-scoped agents that call real tools, with guardrails and a human in the loop wherever being wrong is expensive.
What you get
- Agent architecture and tool definitions
- Guardrails and approval steps
- Conversation and action logging
- Evaluation harness with regression cases
- Cost and latency budgets
What changes
- The agent does a defined job rather than an open-ended one
- Every action it took is reconstructable afterwards
- A wrong answer is caught by a check, not by a customer
- Spend per conversation is known before launch, not after
When teams call us
- A support queue where most tickets are the same ten questions
- An internal process that moves data between three systems by hand
- A workflow needing judgment but not an unsupervised decision
Process
How the work runs
The same four phases whichever engagement model you pick, each ending in something you can read.
Feasibility
Whether a model genuinely beats deterministic code here, tested on your data before anyone commits a budget.
- Data review
- Baseline comparison
- Go/no-go in writing
Evaluation harness
Built before the feature, not after. Without a scoring set you cannot tell a prompt change from a regression.
- Golden test set
- Scoring criteria
- Regression runs
Build & tune
Retrieval, prompts, tools and guardrails developed against that harness, with cost and latency tracked per change.
- Versioned prompts
- Cost per request
- Latency budget
Ship & monitor
Live with logging of inputs, outputs and tool calls, so quality is observed rather than assumed.
- Output logging
- Drift alerts
- Spend caps
AI Agents & Assistants, specifically
The questions that come up on this work in particular.
Still have a question? Talk to the team
Not sure which service your project needs?
Most useful projects cross two or three of them. Describe the outcome and we will map it.