What Agents Are and Are Not
The word agent has been stretched to cover everything from a slightly-more-capable chatbot to a fully autonomous digital worker. That imprecision is expensive. In our practice, an agent is a system that pursues a defined goal by planning steps, invoking tools, observing outcomes, and iterating within explicit boundaries. Everything else is either a workflow or an assistant.
This definition matters because it dictates the engineering. A workflow needs orchestration. An assistant needs prompt design. An agent needs both, plus tool permissioning, transaction logging, evaluation infrastructure, and human oversight modeled on the risk of each action it can take.
How We Design a Production Agent
Scope narrowly, permission tightly
Every agent starts with a single well-defined job and a specific set of tools. Broader scope is earned through demonstrated reliability, not granted upfront.
Classify actions by impact
Read-only tools require different oversight than write tools, and irreversible actions (sending money, deleting records, external communication) require human approval by default. This classification is enforced in code, not in policy.
Instrument for evaluation
Task success rate, tool-call accuracy, intervention rate, and per-task cost are logged from day one. A test set of representative tasks is maintained alongside the agent and re-run on every change.
Design the exception path first
What happens when the agent cannot complete the task — where does it hand off, what state does it preserve, how is the human alerted? The exception path is designed before the happy path.
Where Agents Produce Real Leverage
- Inbound request triage with automated first-response drafting
- Cross-system data reconciliation with structured resolution
- Compliance evidence gathering and preliminary review
- Sales research and account brief generation
- Internal knowledge base curation and gap detection
- Software operations — log triage, incident summarization
- Vendor and contract review with clause library grounding
- Recruiting screen and structured note taking
Risks We Design Against
Agents fail in modes that other software does not. Unbounded reasoning loops produce runaway cost. Silent tool errors compound. Model updates change behavior. Prompt injection through user or document content can redirect the agent's goal. Each of these has an established mitigation — cost ceilings, tool-level error surfaces, evaluation gates on model changes, input classification and privilege separation. None of them is optional in production.
Frequently Asked Questions
What is an AI agent, in practical enterprise terms?
An AI agent is a system that takes a goal, plans a sequence of steps, uses tools to execute those steps across systems, observes results, and iterates. It is distinct from a chatbot (single-turn response) and from a workflow (fixed script) because it decides its own path within defined boundaries.
Are agents ready for production use?
For narrowly scoped, well-instrumented tasks with reversible actions and human oversight, yes. For open-ended autonomous decision-making without supervision, no. The engineering discipline is knowing which is which.
How do you keep agents from taking destructive actions?
Tool-level permissions, action classification (read vs write vs irreversible), human-in-the-loop gates on high-impact steps, transaction logs, and dry-run modes for new capabilities. Agents get access to what they need for the task, nothing more.
What frameworks do you use?
We remain framework-neutral. Depending on the use case we work with the client's existing stack or select from established options for tool orchestration and evaluation. The framework is the least important architectural decision — the design of tools, prompts, and oversight matters far more.
How do you evaluate an agent's performance?
Task success rate against a defined test set, tool-call accuracy, cost per completed task, human intervention rate, and end-user satisfaction. Evaluation is instrumented from day one and reviewed continuously.
Can agents replace SaaS workflows?
In some domains, incrementally. In others, they augment existing SaaS by handling the integration and judgment glue between systems that vendors have not solved. The right question is rarely 'replace or not' but 'where does an agent produce more leverage than a workflow builder.'
What is a realistic first agent to build?
A task-oriented internal agent operating on a defined system boundary — for example, triaging inbound requests, drafting responses using knowledge base retrieval, and creating tickets with structured metadata. Narrow scope, reversible actions, meaningful volume.
What are the biggest risks?
Uncontrolled tool access, silent failure that accumulates over time, cost overruns from unbounded reasoning loops, and drift as the underlying model versions change. All are addressable with governance and instrumentation designed in from the start.