CLIEncoders · AI & Agents
AI agent development
An agent is a system that decides what to do next. That is genuinely useful and genuinely riskier than a chatbot, because the outputs are actions rather than sentences.
What separates an agent from a chatbot
A chatbot returns text and a human decides what to do with it. An agent chooses among tools, calls them, reads the results and continues — which means a mistake changes the state of your business rather than merely producing an awkward paragraph.
That difference drives everything about how these are built. Every tool an agent can reach gets defined with an explicit contract, dangerous operations sit behind confirmation, and the whole trajectory is logged so any action can be explained after the fact.
How we build them
Tools first, agent second. A well-specified set of functions with validated inputs and predictable errors is most of what makes an agent reliable — a capable model wrapped around vague tools behaves worse than a modest model with precise ones.
We expose systems through MCP servers where a standard interface is worth having, orchestrate multi-step flows in LangGraph where the control flow needs to be explicit and inspectable, and use n8n where the work is genuinely integration plumbing that does not need a model in the loop at all. Recognising that last case saves clients a great deal of money.
Where agents are the wrong answer
If a process is deterministic, write the deterministic version. Agents earn their complexity when inputs are unstructured, the path varies, or judgement is genuinely required. Wrapping a fixed five-step workflow in an agent adds latency, cost and a failure mode in exchange for nothing.
We will tell you which parts of your process fall on which side of that line, including when the honest recommendation is a scheduled script.
Questions
What people ask before starting
What stops an agent from doing something destructive?
Architecture, not instructions. Prompt-level rules are not a security boundary. Real controls are narrow tool permissions, irreversible actions gated behind explicit human confirmation, spend and rate caps enforced outside the model, and complete logging. We design the permission surface before the agent logic, and anything that would be unacceptable to do wrongly does not get an unattended tool.
How reliable are agents in practice?
Reliable enough to be valuable on well-scoped tasks, not reliable enough to be unsupervised on high-stakes ones. Success rates fall as the number of steps grows, so we scope narrowly and measure with an evaluation suite rather than estimating. If you need a number for your use case, that suite is how you get an honest one.
Do we need agents, or would automation do?
Often automation would do, and it would be cheaper and more dependable. The test is whether the process needs judgement over unstructured input. Reading varied supplier emails and extracting order details needs a model; moving a record between two systems on a schedule does not.
What does an agent cost to run?
More than a chatbot, because a multi-step task makes several model calls and each carries the accumulated context. That cost is very controllable — caching, routing simple steps to smaller models, and capping steps per task. We model it against your projected volume before you commit, because the demo cost and the production cost differ by more than most people expect.
Related
Where this usually connects
Tell us what you are building
One technical call is usually enough to tell you whether this is straightforward, genuinely hard, or the wrong approach entirely. We would rather say so early than quote for the wrong thing.
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