Multi-step reasoning agents for operations, compliance, and service desks. 4 projects shipped - agents that read tickets, query systems, propose actions, and escalate to humans when confidence drops. We build with LangGraph, CrewAI, and Temporal - instrumented and observable, not autonomous black boxes.
What this is
Most agent demos are autocomplete on hard mode. Production agents have to handle missing data, broken APIs, escalation paths, and audit trails. AiSPRY's agentic practice has been building bounded, observable, interruptible agents since 2024 - designed for operations, not for shareholder decks.
Every agent has an explicit tool catalogue, a maximum step budget, and clear escalation triggers. Unbounded agents are demos; bounded agents are systems.
Read-only and low-risk actions execute autonomously. Anything that touches the world - sending an email, closing a ticket, submitting an approval - goes through human review with full reasoning trace.
Every step the agent took, every tool it called, every decision it made - logged, queryable, replayable. Audit trail isn't a bolt-on; it's the substrate.
What does the agent do when an API times out? When confidence drops? When tools conflict? Designed and tested before any deploy. We use chaos testing on tool layers.
How we do it
Each block is a discipline we've shipped to production. SLA Sentinel uses planning, tool integration, and HITL; Compliance Triage adds multi-agent coordination.
Decomposing a goal into a sequence of tool calls and decisions. ReAct, Plan-and-Execute, and Reflexion patterns depending on the task structure and reasoning depth required.
Wrapping enterprise APIs, databases, and SaaS tools as agent-callable functions. Strict input validation, error handling, and rate limiting baked into every tool.
Orchestrating specialist agents - researcher, planner, critic, executor - for complex workflows. Used selectively, only when single-agent approaches hit complexity ceilings.
Approval gates, review queues, and feedback loops integrated into agent workflows. Production agents always have an escalation path; that path is tested before deploy.
Long-running, durable agent workflows that survive restarts, retries, and partial failures. For operations and compliance work measured in hours or days, not seconds.
Every agent step logged, every tool call traced, every decision auditable. Replay capability for debugging production failures and updating agent behaviour.
Use cases
A representative selection - agentic AI is the newest of our practices. Use cases below are the patterns where we see real production traction.
Agents monitor ticket queues, predict SLA breaches, and route or escalate before the breach occurs. Integrated with ticketing systems, with HITL on auto-escalation actions.
Multi-agent setup that triages incoming compliance documents, classifies them, extracts key facts, and routes to specialist reviewers with pre-filled forms.
Agents handle repetitive queries with grounded answers, escalating to human agents when confidence drops or when the query touches account-modifying actions.
Agents enrich leads from public sources, normalise CRM data, and flag duplicates or stale records. Read-write actions go through human review.
Agents read alerts, query monitoring tools, propose runbook actions, and escalate when confidence drops. Read-only by default; write actions require approval.
Multi-agent setups for systematic literature review, competitive intelligence, and market scanning with structured outputs and source citations.
Tech stack
Selected work
Monitors ticket queues, predicts SLA breaches with ~24-hour lead time, and routes or escalates before breach. HITL gating on auto-escalation; full reasoning trace per decision.
Specialist agents (classifier, extractor, router) coordinate to triage compliance documents. Pre-filled review forms cut reviewer time substantially while preserving full audit trail.
Agents read alerts, query monitoring stack, propose runbook actions, and escalate on confidence drop. Read-only by default; write actions gated through approval workflow.
Frequently asked
Quick answers to what teams ask before bringing us in. Don't see your question? Talk to us directly.
LangGraph is our default for stateful, single-agent workflows - the explicit graph structure makes the agent's logic auditable. CrewAI for multi-agent role-based orchestration. Temporal underneath for long-running, durable workflows that need to survive restarts. AutoGen occasionally for conversational multi-agent setups. We avoid frameworks that hide too much; observability is non-negotiable.
Bounded agency. Every agent has an explicit tool catalogue (no broad-permission "do anything" tools), a maximum step budget, and clear escalation triggers. Read-only and low-risk actions can execute autonomously; anything that modifies state goes through HITL. We test failure modes - API timeouts, conflicting tool outputs, low-confidence states - before deploy, not after.
At decision points, not every step. Agents handle the boring, repetitive work autonomously: querying, classifying, drafting, summarising. Humans review the high-leverage decisions: approvals, escalations, communications going out the door, and any state-modifying action. The interface is built around making review fast - pre-filled forms, reasoning traces visible, single-click approve/reject.
Reasoning traces are first-class. Every step the agent took, every tool it called, every input and output - stored and queryable. We use LangSmith for tracing and have replay capability so we can re-run the failure with updated prompts or tools. Production agents log to OpenTelemetry-compatible stores so they slot into existing observability stacks.
Traditional automation (RPA, workflow tools) handles deterministic, structured processes. Agentic AI is for the messy middle - tasks where the steps depend on the input, where judgement is required at decision points, and where the input itself is unstructured (tickets, documents, emails). If you can write down the exact rules, don't use an agent; if you can't, agents are worth considering.
Three buckets. Task completion rate (did the agent finish the workflow?). Decision quality (did humans approve the agent's recommendations? did escalations happen at the right time?). Operational metrics (cost per task, latency, tool call counts). We instrument every dimension and review weekly during the early production period - agents drift, and the only way to catch it is measurement.
30-minute discussion with our agentic architects. Bring the workflow shape, the decision points, and the escalation paths - we'll tell you honestly whether agents are the right answer.