AI Agents That Complete Workflows, Not Just Conversations — for Finance Ops
AI agents that understand a business’s needs, gather in the right context, use the systems, make decisions within specified boundaries, and complete workflows.
From a focused single-agent workflow to a coordinated multi-agent system, we design, build, integrate, evaluate, and deploy autonomous agents on LangGraph, CrewAI, and AutoGen.
No-obligation scoping session · POC in 2–4 weeks
What Is an AI Agent, and When Does Your Workflow Need One?
An AI agent is a system that plans a sequence of steps toward a goal, uses tools — APIs, databases, documents, other software — to execute those steps, and adapts when a step fails or new information arrives. Where a chatbot answers a question, an agent completes a task: it reads the invoice, checks the PO in your ERP, flags the mismatch, and drafts the exception email.
Agents earn their keep on workflows that are too variable for rules-based automation but too repetitive for skilled staff: multi-step processes with judgment calls, lookups across several systems, and exceptions that follow patterns. The engineering challenge is reliability — bounding what the agent can do, verifying what it did, and keeping a human in the loop for consequential actions.
Perimattic builds agents production-first: scoped tool permissions, structured outputs, evaluation suites that measure task completion rather than chat quality, and full traces of every decision so your team can audit and improve the system over time.
AI Agents Are Arriving Fast. Value Is Arriving Slower.
of enterprise applications will include task-specific AI agents by end of 2026, up from under 5% in 2025 — Gartner
projected global AI agents market in 2026, up from $7.6B in 2025 — a CAGR above 45% through 2030
of organizations report any enterprise-level EBIT impact from AI — McKinsey 2025 State of AI
Adoption does not equal value. The gap is production-grade AI agent development services — and that is the work we do.
What the Research Says About AI Agent Efficiency
Anthropic’s published evaluation of its own research system found that agents with parallel subagents outperformed a single-agent setup by 90.2% on research tasks, and cut completion time for complex queries by up to 90%.
The honest counterweight: agent systems consume multiples of the tokens of a plain chat. Professional AI agent development services earn their keep by scoping agents to workflows where the task value clearly justifies the compute — which is why we baseline the economics per task before building.
Agent Development Capabilities
Six delivery areas covering everything between “we have a workflow” and “an agent runs it in production.”
Single-Agent Task Automation
A focused agent that owns one workflow end-to-end — document triage, data reconciliation, report assembly — with scoped tools and structured outputs.
Multi-Agent Systems
Orchestrator-specialist architectures on LangGraph, CrewAI, and AutoGen: agent communication protocols, shared state, and parallel execution for workflows no single agent can handle.
Tool & System Integration
Agents that act on your stack — ERPNext, Salesforce, SAP, SharePoint, SQL, REST APIs — with authentication, rate limiting, and audit logging built in.
Evaluation & Testing
Task-completion benchmarks, regression suites, and adversarial testing that measure whether the agent finishes the job — not whether it chats well.
Observability & Tracing
Every LLM call, decision, and tool invocation traced in LangSmith or LangFuse, with dashboards your team can read in production.
Human-in-the-Loop Design
Approval gates, escalation paths, and override workflows for consequential actions — so autonomy grows only as trust is earned.
Anatomy of a Production Agent
Every agent we ship follows the same loop — with guardrails and observability wrapped around each stage.
The agent receives a task and gathers context: documents, records, prior state, and the systems it is allowed to read.
It decomposes the goal into steps, selects tools for each, and estimates when to ask for human input.
It executes tool calls — queries, API writes, drafts — each validated against schemas before anything touches your systems.
Outputs are checked against ground truth and policy rules; failures trigger retries, alternative plans, or escalation.
The agent logs the full trace, summarises what it did and why, and hands off anything requiring approval.
Scoped tool permissions, structured output validation, cost and step budgets, human approval gates on consequential actions, and full traces in LangSmith or LangFuse.
Agents That Act on the Stack You Already Run
Forty-five connectors across ERPs, CRMs, HR and ITSM suites, databases, warehouses, commerce, messaging, and model providers — with authentication, rate limiting, and audit logging on every one.
AWS
ShopifyREST API and CSV/SFTP supported for everything else. Ask about a custom connector →
Agent Workflows Running in the Verticals We Serve
Concrete agent deployments across the six industries where Perimattic ships AI — each one a workflow with real volume, real systems, and a human approval gate.
Banking
Healthcare
Manufacturing
Logistics
Retail & E-Commerce
Travel
Where Agents Deliver First
The workflows where agent automation typically produces measurable returns within the first quarter.
Document-heavy operations
Invoice processing, claims intake, contract review triage — agents that extract, cross-check against systems of record, and route exceptions.
Customer operations
Ticket triage and resolution drafting grounded in your policy documents, with escalation to humans on low-confidence cases.
Sales and revenue ops
Lead enrichment, CRM hygiene, quote assembly, and meeting-prep briefs assembled from your CRM, email, and product data.
Finance and back office
Reconciliation, exception investigation, and month-end checklist automation across ERP, banking, and spreadsheet data.
Research and monitoring
Agents that watch sources — filings, competitor sites, internal metrics — and produce structured alerts with evidence attached.
Internal knowledge work
Answering staff questions from your knowledge base with citations, and drafting documents that follow your templates and tone.
IT operations
Incident triage agents that correlate alerts, pull runbooks, draft remediation steps, and open tickets with full diagnostic context attached.
HR and recruiting ops
Screening against job specs, interview scheduling, onboarding checklist automation, and policy Q&A grounded in your HR handbook.
Procurement and supply chain
Three-way match checks, supplier follow-ups on late POs, quote comparison summaries, and contract renewal monitoring.
How Do AI Agents Compare to Traditional Automation?
Rules-based automation and AI agents solve different problems. The winning architecture usually combines both.
| Traditional automation (RPA / rules) | AI agents | |
|---|---|---|
| Handles variability | Breaks when inputs deviate from the scripted path | Plans around variation; interprets documents, exceptions, and ambiguity |
| Latency per task | Milliseconds to seconds | Seconds to minutes — planning and tool calls take model time |
| Failure mode | Hard stop; queue backs up until a human fixes the bot | Retries, alternative plans, or escalation to a human with context attached |
| Cost per task | Near zero once built; high maintenance when systems change | Typically $0.01–$0.50 in model cost per task, falling as models get cheaper |
| When it wins | Fixed, high-volume, deterministic steps | Judgment-heavy, multi-system workflows with exceptions that follow patterns |
Should You Build, Buy, or Go Hybrid?
| Buy off-the-shelf | Build custom | Hybrid | |
|---|---|---|---|
| Upfront cost | $20–$150 per user/month, minimal setup | $12,000–$40,000 for a production system | $5,000–$20,000 — custom agent logic on managed platforms |
| Time to value | Days to weeks | 6–12 weeks to production | 3–8 weeks |
| Fit to your workflow | Generic; your process adapts to the tool | Exact — built around your systems, data, and approval rules | Core workflow custom; commodity steps off-the-shelf |
| Ownership & lock-in | Vendor owns the roadmap and pricing | You own the code, prompts, and evaluation data | You own the differentiating logic |
What Is the Cost of Implementing AI Agents for My Business?
Published bands, before any sales call. Every engagement is scoped and quoted in writing before work begins.
One scoped workflow, real data, agreed success metrics. You see the agent complete real tasks before committing further.
Full integration, evaluation suites, observability, and human-in-the-loop workflows. Complex multi-agent systems range up to $75,000; ongoing costs typically add 15–25% of build cost annually.
Monitoring, evaluation regression runs, prompt and model updates, plus model API costs — typically $0.01–$0.50 per completed task.
A Consulting Firm Cuts Research Time by 65% With an Agent-Driven Knowledge System
The situation
A major real-estate consulting firm held decades of deal documents, market reports, and contracts across SharePoint. Finding precedent for a new engagement took analysts up to a full day of manual searching, and document drafting started from scratch each time.
What we built — and what broke
A RAG pipeline over SharePoint with prompt-based search, plus a drafting agent that assembles first-pass documents from retrieved precedent. Mid-build, retrieval quality collapsed on scanned legacy files — we added an OCR pass and re-chunked the corpus, the kind of issue that only surfaces on real client data.
The outcome
Time to find critical information dropped 65%. Document preparation times fell 50%. Research cycles shrank from a day to hours, with every generated draft citing its sources for review.
Read the full case study →AI Built. Delivered. Running.
Real engagements from our case-study library — the system delivered and the outcome it produced.
AI Agent Development, Answered
What is the difference between an AI agent and a chatbot?
A chatbot responds to messages. An agent pursues a goal: it plans steps, calls tools and APIs, checks its own results, and finishes a task — often without a conversation happening at all. Chat can be one interface to an agent, but the value is in the completed workflow, not the dialogue.
How do you keep agents from taking wrong actions?
Four layers: scoped permissions (an agent can only call the tools it is given), schema validation on every output before it touches a system, step and cost budgets that halt runaway loops, and human approval gates on consequential actions like payments, deletions, or external communications. Every action is logged and traceable.
Which frameworks do you build on?
LangGraph for stateful, controllable single- and multi-agent graphs; CrewAI for role-based agent teams; AutoGen for conversational multi-agent patterns. Model choice — GPT-4o, Claude, or open-source via Ollama — is driven by your accuracy, latency, cost, and data-residency requirements.
How much does agent development cost?
A focused single-workflow agent POC starts from around USD 3,000. A production agent with integrations, evaluation suites, observability, and human-in-the-loop workflows is typically USD 12,000 to USD 40,000 depending on the number of systems touched. Multi-agent systems are scoped case by case. Every engagement is scoped and quoted before work begins.
Is our data used to train models?
No. Agents run on API-based or self-hosted models under enterprise terms where your data is not used for training. For stricter requirements we deploy open-source models on your own infrastructure so no data leaves your environment. All credentials are scoped, and every data access is logged.
How do agents fit with our existing RPA or automation?
They complement it. Rules-based RPA stays for fixed, deterministic steps; agents take the judgment-heavy segments — interpreting documents, resolving exceptions, deciding routing — and can trigger or be triggered by your existing RPA flows, schedulers, and webhooks.
How do we measure whether the agent is working?
We define task-level metrics before the build: completion rate, exception rate, human-override rate, cycle time, and cost per task. The agent is benchmarked against your current process on a held-out sample, and production dashboards track the same metrics continuously after go-live.
How do you compare tools for AI agent observability and audit trails?
LangSmith offers the deepest LangChain/LangGraph trace integration and evaluation tooling; LangFuse is open-source and self-hostable, which suits data-residency requirements; OpenTelemetry fits when agent traces must flow into your existing APM stack (Datadog, Grafana). We typically pair LangSmith or LangFuse for agent-level traces with OpenTelemetry export for enterprise observability.
How long until a working agent?
A proof-of-concept agent on a well-scoped workflow typically takes two to four weeks. Production hardening — integration, evaluation suites, observability, and human-in-the-loop workflows — typically brings the total to six to twelve weeks depending on the number of systems the agent touches.
Have a Workflow an Agent Should Be Running?
Describe the process — the systems it touches, the judgment calls it involves, the volume it handles. We will map it to an agent architecture and scope a working proof of concept.