AI agents that are allowed to act only where action is safe

For teams whose assistant has to use internal tools, not only write text.

Tool-using agents for bounded workflows, with permissions and evals – not an autonomous employee.

You leave with

An agent with caps and evals

An architecture decision: workflow, router, or agent – with reasons.
Tool contracts and a permission model.
A runnable agent or copilot with traces and caps.
An eval set the team can run in CI.

See it done

Synopsys · LLM evaluationBenchmarking and comparing large language models Genie Platforms · AI SDRConversational AI running as production services Mindshine · TIVAVoice and text retrieval across any data source

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I build AI agents for companies that have a workflow with tools a model can call – search, tickets, CRMs, internal APIs – and that cannot afford unsupervised side effects. An agent is not a smarter chatbot. It is a loop: observe, choose a tool, validate the result, stop. The engineering is in the tools, the permissions, the stop conditions, and the evaluation set. I will recommend a single-shot structured call or a deterministic workflow when an agent would only add a loop around a task that did not need one. Reliability is the product; autonomy is a risk you take on purpose.

Who this is for

Product teams adding an assistant that must use internal tools, not only generate text.
Operations teams with repetitive multi-step work that still needs judgement.
Platform teams who have been asked for 'an agent' and need to reduce it to an architecture.

Problems agents are for – and are not

A copilot must read from several systems and propose a next action.
The prototype agent loops, double-books, or emails the wrong person.
Leadership wants agents; the domain needs a state machine with an LLM at one step.

What I design

Tool interfaces with typed inputs, idempotency, and least privilege.
Planner-versus-executor splits so the model cannot both invent and commit.
Stop conditions, timeouts, and budget caps on tool calls.
Evaluation: scripted trajectories, failure taxonomies, and regression sets.
Human approval for irreversible actions.

How I decide whether you need an agent

If the steps are known, use a workflow. If the next step depends on messy input but the tools are few, use a router. If the model must discover a path through many tools, then – and only then – an agent loop, with a hard cap and an audit log. Most commercial 'agent' requests collapse to a router plus three tools.

What makes an agent reliable enough to ship

Reliability is not a prompt. It is the same discipline as any distributed worker, plus a probabilistic planner.

Permissions are not prompt text

A model instructed 'do not delete' will still call delete if the tool is available. Enforce in the tool layer.

Every tool call needs a receipt

Idempotency keys, structured errors, and traces. An agent that cannot explain which tool ran is not debuggable.

Cap the loop

Unbounded ReAct is a cost and safety incident. Set a maximum steps, a wall clock, and a dollar budget.

Evaluate trajectories, not vibes

Hold-out tasks with known good tool sequences. If you cannot write ten, you cannot tell whether a change helped.

Separate propose from commit

Draft the ticket; a human or a rules gate publishes it. The more irreversible the action, the stricter the gate.

Agent failure modes

Giving the model a browser and production credentials 'to be helpful'.
No simulation environment, so evals hit live customers.
Treating multi-agent debate as a substitute for a specification.
Hiding a workflow behind an agent because the workflow was never designed.

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Perguntas que me fazem

When should you use an AI agent?

When the next action cannot be tabulated in advance, the tools are well-defined, and failure is recoverable or gated. If you can write the flowchart, write the flowchart. Agents are for residual ambiguity, not for skipping product design.

Reduce 'we need an agent' to an architecture

Describe the tools, the irreversible actions, and what a wrong loop would cost.