AI agent development company for agents that work in production

What an AI agent is, and when you need one

A chatbot answers questions. An agent gets things done: it decides which steps are needed, calls tools to fetch data or take actions, checks the result, and continues until the task is complete or it needs a human. Agents earn their keep on tasks that are frequent, rule-heavy and currently done by people copying information between systems.

How we build agents

1. Task definition. We write down the exact task, the inputs, the allowed actions, and what a correct outcome looks like. If we cannot describe success, we do not build yet.
2. Retrieval. Most agents need your data. We build permission-aware retrieval over documents and databases, with chunking and indexing tuned to your content, not defaults.
3. Tools. Each action the agent can take becomes a typed, tested function with input validation, rate limits and an audit log.
4. Evaluation. Before launch we build an evaluation set of real cases and score the agent on it. This is how we catch regressions when models or prompts change.
5. Guardrails. Confidence thresholds, human-in-the-loop for high-impact actions, spend caps, content filters and prompt-injection defences.
6. Monitoring. Traces of every run, cost per task, success rate and a feedback loop so the evaluation set grows from production.

What it costs

Running costs are separate and depend on model choice and volume. A support agent handling 10,000 conversations a month on a mid-tier model typically costs a few hundred dollars in model fees; we design for cost from the start with caching, smaller models for routing, and hard spend limits.

Model choices

We work with OpenAI, Anthropic and Google APIs, and with open models such as Llama and Mistral when data residency or cost demands it. The choice is made per task: a large model for planning and hard cases, a smaller or open model for classification and routing. Nothing in the architecture ties you to one vendor, and the evaluation set makes switching measurable.

Security and data privacy

• Models can run through your own cloud account or in your VPC; prompts and data are logged under your control.
• Retrieval respects your existing permissions, so an agent never surfaces documents a user cannot see.
• Every tool call is validated, rate-limited and audit-logged.
• High-impact actions such as payments or deletions require human approval by default.
• NDA and, for EU clients, a data processing agreement before any access.

Signs an agent project will fail

• The task cannot be described precisely enough to write test cases for it.
• The data the agent needs is not accessible through an API or export.
• Nobody owns the outcome once the agent is live.
• The plan is to ship without an evaluation set and 'see how it goes'.

We say no to projects with these signs, or we run a two-week feasibility sprint first. Innovation Insight's AI practice has shipped assistants, agents and models into production since 2023 and works alongside our web, mobile and cloud teams so the agent lands inside a product, not beside it.

A worked example: a support agent

A subscription software company receives 6,000 support tickets a month. Around 40 percent are password resets, invoice requests, plan changes and 'where is my export' questions. We build an agent with four tools: look up the customer, resend an invoice, change a plan within policy, and open an escalation with a summary. It answers from the help centre through retrieval, acts on the tools when confident, and hands anything else to a person with the context already gathered.

Model fees for this volume are typically $200 to $600 a month. The agent is measured on resolution rate, escalation accuracy and customer satisfaction, and the evaluation set grows with every reviewed conversation.

Agent architecture, in plain terms

1. An orchestrator receives the task and decides the next step.
2. A retrieval layer supplies relevant documents and records, filtered by the user's permissions.
3. Typed tools expose specific actions with validation and audit logging.
4. A policy layer enforces spend limits, confidence thresholds and approval rules.
5. Traces and metrics feed a dashboard and the evaluation set.

This structure is the same whether the agent runs on OpenAI, Anthropic, Google or an open model, which is what keeps you free to change vendors.

Human in the loop

Autonomy is a dial, not a switch. Early on, the agent proposes and a person approves. As the evaluation set shows reliable performance on a class of actions, that class moves to automatic with sampling review. High-impact actions such as refunds above a threshold, deletions or external communications can stay on approval indefinitely. The dashboard shows the approval rate by action type so the dial is turned with evidence.

Where agents pay back fastest

• Support and success teams with high volumes of repetitive tickets.
• Back-office operations that move information between systems by hand.
• Sales development: research, enrichment and first-draft outreach.
• Engineering and IT: triage, runbook execution and reporting.
• Compliance and finance: document review, reconciliation and exception flagging.

Evaluation, in more detail

An evaluation set is a list of real inputs with expected outcomes: the ticket and what a good resolution looks like, the document and the fields that should be extracted, the question and the correct answer with its source. We build the first set from history during week one and score the agent on it automatically, reporting accuracy, tool-call correctness, escalation precision and cost per task. Every prompt, model or tool change is run against the set before it ships, and reviewed production runs feed back into it. This is the single practice that separates agents that improve from agents that quietly degrade.

Working with your team

We can build and operate the agent, or build it and hand it to your engineers with the evaluation harness, dashboards and a workshop on safe changes. Either way you own the code, prompts and data, and the architecture does not lock you to a model vendor.

Costs to plan for after launch

Three costs continue after an agent ships: model fees, which scale with volume and are usually modest; monitoring and tuning time, covered by a retainer of $1.5k to $8k a month; and the human review that keeps the evaluation set honest, typically an hour or two a week from someone on your team. We report all three on the dashboard so the return on the agent is visible, not assumed.