AI, Data & Emerging Technology · AI agents
AI agents that complete real work, safely
We design and build AI agents that read requests, plan the steps, call your systems through approved tools and hand off to a person when judgment is needed, so routine work gets done without losing control.
- Tool and API calling
- Planning and memory
- Human-in-the-loop checkpoints
Overview
What makes an AI agent different, and harder
An agent is a language model that can take actions: look up a record, call an API, update a ticket, send a draft for approval. Instead of answering one question, it works through a multi-step task, choosing which tool to use next based on what it has learned so far. That flexibility is the value, and it is also the risk.
The key design choices are how much autonomy to grant, which actions need a human sign-off, and what the agent should do when it is unsure. A tightly scripted workflow with a model at two or three decision points is often more reliable than a free-roaming agent. We pick the least autonomy that still removes the tedious work. Autonomy can always be widened later.
A well-built agent leaves a readable trail of every step, stops cleanly when it hits a limit, and can be tested against a fixed set of scenarios whenever the model or the tools change. It should also be easy to pause. One switch should stop new actions while leaving logs and pending approvals intact for a person to review.
Who it’s for
Built for teams like yours
- 01
Back-office operations teams
Teams running repetitive multi-system processes, such as order exceptions, vendor onboarding or account updates, where each case needs a few lookups and a judgment call before the next step.
- 02
Customer support leaders
Support managers who want routine requests like refunds, address changes or status checks resolved end to end, with clear escalation to a person when policy or tone requires it.
- 03
Engineering and IT groups
Internal technical teams that want agents to handle access requests, incident summaries or routine maintenance tasks inside the guardrails they already enforce for human staff.
Why it matters
Automation that can handle the messy middle
Rules-based automation breaks the moment an email is phrased differently or a record is missing a field. An agent can read the request, decide which tools to use, check its own output and ask for help when it is unsure. That makes it useful for the in-between work that scripts and forms never quite covered.
We scope agents narrowly, give them only the permissions they need and log every action, so you can see what happened and why before you widen their reach to new tasks or systems.
Every engagement includes
- Task mappingwe document the workflow, inputs, decisions and failure cases the agent must handle.
- Tool designclean, permissioned functions that let the agent act inside your existing systems.
- Model selectiona reasoned choice between hosted and open models based on cost, latency and data rules.
- Guardrails & approvalslimits, fallbacks and human checkpoints wired in from the first release.
- Evaluation suitescenario tests you keep, so future changes are measured instead of guessed.
- Handoverdocumentation, runbooks and access to every account, prompt and line of code.
Features
How we build agents
- 01
Tool and API calling
Agents act through defined functions in your CRM, ticketing, calendar or database, never through open-ended system access.
- 02
Planning and memory
Multi-step reasoning with task state and short-term memory so long jobs survive interruptions and retries.
- 03
Human-in-the-loop checkpoints
Refunds, outbound emails and record changes can require a person to approve before the agent proceeds.
- 04
Scoped permissions
Each agent runs with least-privilege credentials, rate limits and spending caps you set and can revoke.
- 05
Evaluation harness
A test set of real scenarios scores every prompt or model change before it reaches production users.
- 06
Full action logs
Every decision, tool call and response is traced, searchable and exportable for review or audit.
In practice
Work agents can take on
Order exception handling
When a shipment is late or a payment fails, the agent checks order, inventory and carrier systems, drafts the customer message and proposes a fix, waiting for approval before anything is refunded or reshipped.
Research and briefing packs
Before a sales call or vendor review, the agent gathers account history, recent notes and public information into a short brief with sources, saving staff the tab-switching that eats their morning.
Account maintenance requests
Routine requests like contact changes, plan adjustments or user access are verified against policy, executed through scoped API calls and logged, with anything outside the rules routed to a person.
Data cleanup across systems
The agent works through duplicate or inconsistent records in your CRM or ERP, proposes merges with reasons, and applies the approved changes in batches you can review and roll back.
Process
How we work
- 1
Workflow teardown
We sit with the people doing the task and record every system touched, decision made and exception seen, then mark which steps an agent could take and which stay human.
- 2
Tool contracts
Each action becomes a narrow, typed function with its own permissions, input checks and rate limits, so the agent can only do what the contract allows, nothing broader. Credentials are stored outside the prompt entirely.
- 3
Agent loop build
We implement the planning loop, memory and stopping rules, choosing between a scripted graph and an open loop based on how varied the real cases are. Every loop has a hard cap on steps and spend.
- 4
Scenario testing
The agent runs against a library of recorded and synthetic cases, including tricky and adversarial ones, and each run is scored on outcome, steps taken and cost. Each failure found becomes a new permanent test case.
- 5
Supervised rollout
It starts in suggest-only mode where people approve every action, then earns more autonomy step by step as logs show it handling each case type correctly. Autonomy can be pulled back at any time.
Deliverables
What you receive
- Mapped workflow with automation boundaries
- Permissioned tool and API layer
- Working agent with approval checkpoints
- Scenario library and scoring harness
- Step-by-step action trace viewer
- Escalation rules and fallback behavior
- Runbook for pausing and updating the agent
Tools & methods
Frameworks
- LangGraph
- OpenAI Agents SDK
- Model Context Protocol
- Temporal
- Pydantic
Models
- Claude
- GPT models
- Gemini
- Llama
Infrastructure
- Python
- TypeScript
- PostgreSQL
- Redis
- AWS
- OpenTelemetry
FAQ
Frequently asked questions
Anything else about AI agents? Ask us directly.
A chatbot answers questions. An agent takes actions: it can look up an order, update a record, draft and send a reply or open a ticket, chaining several steps together toward a goal. Because it acts, an agent needs tighter permissions, logging and approval steps, which is where most of our design effort goes.
Let’s work together
Have a project in mind?
Book a strategy call and we’ll show you exactly how to turn your goals into a system that generates consistent results.