Capabilities
The guardrails, and the engineering that backs them.
Two of these guardrails go into every automation I build. The other three are designed-in standards, scoped to what an error in your workflow would cost. The reliability spine is the same either way.
Built into every automation
Two guardrails go into everything I build. Below each is how it shows up in real code.
Confidence-gated human review
The system acts on what it's sure about and routes the rest to a person before it takes effect.
- Approval gates that pause an agent before a consequential action — a person decides, never the model.
- Read-only-by-default tools, with writes explicitly gated.
- Post-response classifiers that tier every output and escalate the risky ones for sign-off.
Cost ceilings
Hard spend limits so a runaway loop or adversarial input can't quietly run up a bill.
- Budget trackers that abort new work mid-run when spend crosses a threshold.
- Per-model token and cost ledgers, persisted for after-the-fact review.
- Per-user rate limiting that holds the line under concurrent load.
Scoped to the stakes
Three more I dial up in proportion to what an error in your workflow would cost — financial-grade rigor where it’s warranted, kept lighter where it isn’t. Below each is how I build it when the stakes call for it.
Eval suites with pass thresholds
Tested against known cases and held to a defined accuracy bar before it ships.
- Real harnesses spanning categories and difficulty tiers, not a handful of happy-path checks.
- Graded on more than one axis: did it take the right action and reach the right answer.
- Run-to-run regression diffing that catches when a change quietly makes things worse.
Structured output validation
Malformed or hallucinated responses are caught at the boundary, not downstream.
- Strict schemas enforced on model output, with correction-or-escalate on failure.
- Forced, deterministic extraction where the shape of the answer must be exact.
- Guards that reject vague or uncited output instead of passing it through.
Fallback and escalation paths
When it can't finish confidently, it has a defined route — it never fails silently.
- Retries with backoff and failover to a secondary provider.
- Graceful degradation and self-healing recovery for long-running services.
- Offline queues with bounded retries so nothing is dropped on the floor.
What makes the output trustworthy
Beyond the guardrails, these are the engineering practices I build in so a business can stand behind the output.
Tamper-evident audit trails
Hash-chained logs that detect tampering and localize exactly where it happened — so a system's history can be trusted, not just its outputs.
Regulated-industry compliance, in code
Data-residency controls, row-level access, pseudonymisation, append-only consent records, and right-to-erasure handling built into the system, not bolted on.
Observability as standard
Per-run execution traces, structured logging, and cost ledgers — you can prove what an automation did, when, and at what spend.
Vendor independence
Pluggable model backends — swap between hosted providers and local models without rewriting the tool logic. No lock-in to one vendor's pricing.
Adversarial hardening
Prompt-injection guards, server-side stripping of client-controlled trust fields, and input defenses — the system assumes it will be probed.
Background
My focus is software for work where a mistake is a liability, not just a bug — where “it usually works” isn’t good enough. That’s the discipline I bring to LLM automation: treat reliability as the product, not a feature. The same patterns that keep a high-stakes system honest are the ones that make any automation safe to leave running.