AI Agents | Mindlytic AI

Real agents — boring in the best way.

We build agents that do exactly one job exceptionally well. Triage an insurance claim. Qualify an inbound lead. Run a research deep-dive and draft the memo. Each agent has a clear scope, a written rubric, and an evaluation harness that runs on every deploy.

We reject the 'general AI assistant' framing. Open-ended agents are unmeasurable, unshippable, and unhappy to own in production. Closed-ended agents are measurable, shippable, and — after two sprints of tuning — quietly better than the humans they replace at repetitive work.

We use LangGraph for orchestration, our own runtime for stateful memory, and Temporal for anything that must survive a restart. Every tool call is logged, replayable, and testable.

Twelve capabilities we ship by default.

Tool routing

Pick the right tool from a library of 50+ integrations with budget-aware planning.

Stateful memory

Short-term working memory + long-term embeddings indexed by user, thread, and task.

Parallel sub-agents

Fan out to specialist agents, aggregate, and return. Token-cost budgeting included.

Human-in-the-loop

Checkpoint to a human for approval on anything over a configurable risk score.

Structured output

Pydantic / Zod-validated output with automatic retries and schema repair.

Eval harness

Golden sets, judge models, regression alerts on every merge to main.

Observability

OpenTelemetry traces, token costs per request, and per-tool latency histograms.

Safety guards

Prompt-injection defense, PII redaction, jailbreak detection, policy enforcement.

Cost controls

Per-tenant token budgets, auto-fallback to cheaper models under load.

Multi-model

Claude / GPT / Gemini in the same runtime; pick per-step based on capability needs.

Audit logging

Every decision, tool call, and output — queryable, exportable, compliance-ready.

Graceful failure

Circuit breakers, dead-letter queues, and human escalation paths built in.

Five-layer reference architecture.

The pattern we've shipped across 127 production agents. We start here and customize — never the reverse.

→Intake layer: normalize channels (chat, email, call, API) into a common event.
→Planner: route to a specialist agent based on intent + context + risk score.
→Executor: run tools via a controlled sandbox with token + time budgets.
→Verifier: check output against the rubric; retry, repair, or escalate.
→Persister: write decisions, traces, and audit events to durable storage.

Agents that finish the work, not just start it.