Building an AI agent demo takes days. Getting AI agents into production takes a different kind of work. The model is rarely the problem. Pilots stall because nobody built what surrounds it: reliable access to tools and data, workflows that survive failures, permissions, evaluations, monitoring and a budget for what the agent is allowed to spend.
We call that surrounding system the agent harness. This guide covers what goes into one, the order to build it in, and the decisions that separate a proof of concept that impresses from an agent that keeps working on a Tuesday afternoon when nobody is watching.
The short version
- Start with a workflow, not an agent. Add autonomy only where it pays.
- Write evaluations before features, using real inputs.
- Connect tools through a standard such as the Model Context Protocol.
- Run long tasks on durable workflows that survive restarts and retries.
- Gate risky actions behind permissions and human approval.
- Budget tokens like capital and make the prove phase a paid, measured step.
Why AI agent pilots stall
- Happy-path demos. The pilot worked on curated examples and nobody measured the rest.
- Fragile integrations. Tool calls were hard-coded against one system and break on the second.
- No state. A task that takes hours or waits for a human cannot live in a single request.
- No owner for risk. Security and compliance see the agent for the first time at launch review.
- Unknown cost. Nobody can say what a thousand runs will cost or what they are worth.
What is an agent harness?
An agent harness is the infrastructure that lets a language model act safely and reliably inside a business: orchestration of steps, access to tools and data, durable state, guardrails, evaluation, observability and cost control. The model supplies judgment; the harness supplies everything that makes that judgment usable at scale.
Start with a workflow, then add autonomy
Anthropic’s guidance on building effective agents makes a useful distinction between workflows, where code orchestrates model calls along a defined path, and agents, where the model decides its own next steps. Most production value comes from workflows with a few well-chosen agentic steps. Start with the simplest structure that solves the problem and add autonomy only where the extra flexibility is worth the extra risk.
Evaluations come first
Before adding features, collect a set of real inputs with known good outcomes and score the agent against them on every change. Include the awkward cases: incomplete records, conflicting documents, requests the agent should refuse. An evaluation suite turns “it seems better” into a number, and it is the only reliable way to know whether a model upgrade or prompt change helped.
Connect tools through a standard
Agents are only as useful as the systems they can reach. The Model Context Protocol (MCP) gives models a consistent way to discover and call tools and data sources, so an integration built once can serve several agents and several AI platforms. Scope each tool narrowly: an agent that can read invoices does not also need permission to approve them.
Durable workflows for long-running work
Real business tasks wait on people, retry failed calls and run for hours or days. Running them inside a single web request guarantees lost work. A durable execution engine such as Temporal records each step so a task can resume exactly where it stopped after a crash, a deploy or a human approval that arrives the next morning. Retries need care, though: any step that touches an outside system (sending an email, charging a card, updating a record) should be idempotent, using a unique key so that running it twice has the same effect as running it once.
Guardrails, permissions and human approval
- Give each agent its own identity and the least privilege it needs.
- Require human approval for actions that move money, change customer records, send external messages or touch production systems.
- Validate tool inputs and outputs, and treat anything retrieved from documents or the web as untrusted.
- Log every step, prompt and tool call so decisions can be reviewed and audited.
Budget tokens like capital
Every agent run has a cost in tokens, tool calls and human review time. Treat that spend like any other investment: set a budget per task, measure the value each successful run creates, and stop or redesign flows where the cost exceeds the return. We recommend making the prove phase a paid, time-boxed step with agreed success metrics. It forces a real business case and gives everyone permission to stop if the numbers do not work.
What this looks like in practice
In prior roles, our leadership has taken this path in several industries. In enterprise technology, an agent proof of concept built on LangGraph and LangChain became the starting point for a broader automation program. In cybersecurity, the team designed a customer-lifecycle agent harness on a major cloud platform, a cloud data warehouse and durable workflows, so new agents could share tools, state and controls rather than each reinventing them. In clinical research, the first agent pilot was chosen for a measurable outcome (predicting appointment no-shows) and funded as a paid prove phase before any build commitment.
Your harness checklist
- One workflow, one measurable outcome, one owner.
- An evaluation set of real inputs, run on every change.
- Tools exposed through MCP with narrow, per-agent permissions.
- Durable execution for anything that waits, retries or runs long.
- Human approval on high-risk actions and full audit logs.
- A token and review budget per task, compared against the value created.
Frequently asked questions
What is an agent harness?
An agent harness is the infrastructure around a language model that lets it act reliably in a business: orchestration, tool and data access, durable state, guardrails, evaluations, observability and cost control.
Should I build an AI agent or a workflow?
Start with a workflow where code controls the path and the model handles specific steps. Add agentic decision-making only where the flexibility is worth the extra risk and cost.
What is the Model Context Protocol?
The Model Context Protocol (MCP) is an open standard for connecting AI models to tools and data sources, so integrations can be built once and reused across agents and AI platforms.
How do you measure an AI agent before production?
Build an evaluation set of real inputs with known good outcomes, score the agent on accuracy, safety and cost, and compare every change against that baseline.
How long does it take to get an AI agent into production?
A focused agent with a clear outcome can move from proof of concept to production in weeks, provided evaluations, integrations and approvals are planned from the start.
Have an agent pilot that needs to become a production system? See our enterprise AI adoption services or tell us about your use case.