Non-determinism breaks the SDLC. The same input can produce different output. You can’t unit-test your way to confidence; you need sampling, rubrics and statistical gates.

Failure is silent and plausible. Agents don’t throw exceptions. They drift, loop, hallucinate a field, or take a reasonable-looking wrong action against a live system.

The model is the easy part. Most of the build is tool contracts, state, retries, idempotency, permissions, error paths and integration with systems that were designed for forms, not agents.

Autonomy needs decision rights. “How much can it do on its own” is an architecture decision, not a setting. Get it wrong and you either ship something nobody will approve, or something that asks a human about everything and saves nothing.

Cost and latency are design constraints. Token spend, context size, model choice per step and caching decide whether the thing is economical at volume.

Agent architecture — Break work into focused, testable agents. Decide what belongs to AI, rules, or deterministic services.

Orchestration — Manage chaining, branching, parallel work, retries and state across systems and long-running workflows.

Tool & MCP design — Build typed, permissioned tools with clear contracts, idempotent actions and controlled failure handling.

Context & retrieval — Engineer what agents see and how information is retrieved, ranked and grounded.

Guardrails & decision rights — Define deterministic checks, thresholds, approval gates and where humans must decide.

Evaluation — Build evals and release gates into delivery to measure behavior before and after deployment. Powered by EvalWorks.

Observability & audit — Trace inputs, retrievals, tool calls, actions and outputs for explainability and auditability.

Cost, latency & models — Select models, caching and fallbacks based on performance and cost-per-transaction targets.

Operations — Manage versioning, rollout, drift, incidents and production runbooks for reliable agent operations.

AcxWorks delivery lifecycle. Analysis, design, development, testing, deployment and operations, with AI inside each stage and engineering controls and measurement throughout. Agents help us build; controls keep it honest.

Claude-trained engineers in pods. Small pods pairing agentic engineers with insurance SMEs, so domain judgment reaches the build instead of arriving in a requirements document.

Evaluation-driven development. The eval suite is written alongside the agent, not after it. If we can’t measure a behavior, we don’t consider it delivered.

Thin slice first. One workflow, end to end, in production, with guardrails and evals — then widen. Horizontal build-outs stall.

Bring our IP — KognitiveWorks. Pre-built, insurance-tuned agents and workflows. Fastest path to value, accelerators out of the box, lower build cost on proven patterns, and a platform we improve continuously.

Use your framework. AWS Bedrock AgentCore, Azure AI, LangGraph, CrewAI, or whatever your architecture group has settled on. Fits your cloud, security and governance. No platform lock-in — you own the IP.

Same engineers, same delivery discipline, same controls. The choice changes the build cost and the ownership model, not the quality bar. We’ll tell you which one we’d pick for your situation and why.

We build systems of action across the insurance value chain — submission and appetite, pre-underwriting enrichment, policy operations, risk assessment, claims. The engineering is the same discipline; the domain is where it pays.