Production AI Agents and Workflow Automation

AI Agents and Workflow Automation Services
Agents That Can Act Safely Inside Real Workflows

Devlyn designs and builds AI agents that do more than chat: they retrieve context, call tools, prepare decisions, request approval, update systems, route exceptions, and leave an audit trail. The work is engineered around permissions, state, retries, human oversight, evaluation, and operational control.

Scope an agent workflow See agent delivery scope

Scoped tool access

Permissions and approvals

Durable workflow state

Retries, replay, recovery

Traceable execution

Logs, evals, handover

Why AI agent projects fail when they stop at a demo

An agent demo can look impressive while still being unsafe for production. Real workflow automation needs task boundaries, credentials, action previews, approval rules, recovery behavior, and auditability. Otherwise the agent becomes an unmanaged automation layer with model unpredictability added on top.

What breaks

Agents get broad tool access without task-level permission boundaries, approval gates, or credential ownership.

Long-running tasks fail because state, retries, cancellation, idempotency, and recovery behavior were not designed.

Human review is bolted on as a message instead of a structured approval step with the actual action payload visible.

No one can reconstruct what happened across prompts, retrieved context, tool calls, API responses, model decisions, and user approvals.

The first successful demo hides failure modes: prompt injection, incorrect tool selection, partial completion, duplicate actions, and cost spikes.

How Devlyn reduces risk

We define the workflow boundary, agent role, allowed tools, input contracts, output contracts, approval points, and escalation paths before implementation.

Agent workflows are built with durable state, retries, replay, cancellation, error handling, and clear exit conditions.

Tool access is scoped by workflow and action risk, with validation, authorization, audit logs, and human approval where needed.

Evaluation includes normal paths, edge cases, adversarial prompts, failed tools, partial data, and blocked actions.

Your team receives traces, runbooks, tool contracts, architecture notes, known limitations, and handover documentation.

What we deliver in AI agents and workflow automation

The service is designed around workflows where AI must coordinate steps, tools, systems, and people. The agent is only useful if the surrounding control plane is clear.

01

Workflow and agent architecture

We model the workflow, agent role, steps, state transitions, decision points, tool calls, human approvals, exceptions, and completion criteria.

02

Tool contracts and integrations

We define tool schemas, API contracts, credentials, scopes, validation, rate limits, retries, and rollback expectations for each connected system.

03

Human-in-the-loop controls

We design approval steps that show the action payload, context, risk, and recommended decision before an agent commits a sensitive operation.

04

Memory and context design

We define what the agent can remember, retrieve, summarize, store, forget, and expose so context helps the task without leaking sensitive information.

05

Agent evaluation and observability

We build task suites, trace review, tool-call inspection, success criteria, cost visibility, blocked-action review, and regression checks.

06

Production handover

We document architecture, workflows, tool registry, approvals, dashboards, runbooks, failure modes, and ownership boundaries.

Agent workflow automation capabilities

Capabilities are chosen based on the workflow risk. A support triage agent, an invoice exception agent, and a compliance evidence agent should not share the same autonomy model.

Back-office workflow agents

Back-office workflow agents

Automate repetitive operations such as intake, lookup, triage, exception routing, evidence collection, approval preparation, and system updates.

Customer and employee support agents

Customer and employee support agents

Create agents that retrieve policy or account context, answer questions, prepare actions, escalate edge cases, and preserve conversation history.

Enterprise system action agents

Enterprise system action agents

Connect agents to CRMs, ERPs, ticketing systems, finance tools, document systems, and internal APIs through governed action boundaries.

Research and analysis agents

Research and analysis agents

Build agents that collect data, compare sources, summarize findings, prepare drafts, and leave reviewable evidence for human decision-makers.

MCP and tool gateway design

MCP and tool gateway design

Design controlled access layers for tools, services, and APIs so agents can act through consistent schemas, authorization, and audit controls.

Agent operations and improvement

Agent operations and improvement

Track task success, failure reasons, tool errors, latency, cost, user feedback, blocked actions, and new automation opportunities.

How the AI agent engagement runs

The engagement starts with workflow risk, not a blank agent canvas. We design the control model before enabling autonomous or semi-autonomous action.

We document the current process, systems, users, data, decisions, exceptions, approvals, and pain points.
Map the workflow
We decide which steps are AI-assisted, which can be automated, which require approval, and which should stay human-owned.
Define action boundaries
We define tool schemas, credentials, state handling, retries, cancellation, logging, and failure recovery.
Design tools and state
We implement the workflow through visible increments and test normal paths, edge cases, failed tools, bad inputs, and blocked actions.
Build and test task paths
We instrument traces, task completion, tool calls, errors, cost, approval outcomes, and regression scenarios.
Add evaluation and observability
We prepare runbooks, dashboards, approval docs, support notes, known limitations, and ownership boundaries for production use.
Launch with handover

AI agent engagement models

Start small when the workflow risk is high. Expand only after the first agent proves useful, observable, and controllable.

Discovery

Agent Workflow Assessment

Best when the workflow and risk boundaries are unclear

Scoped

after discovery

Workflow mapping

Tool and data review

Risk and approval model

Build recommendation

Talk to Sales
Most Popular

Build

Single Workflow Agent

Best for one controlled workflow with measurable value

Scoped

after discovery

Agent architecture

Tool contracts

Human approval

Trace and eval suite

Talk to Sales

Program

Agent Workflow Program

Best for multiple connected workflows or departments

Scoped

after discovery

Agent portfolio roadmap

Shared tool governance

Observability model

Ongoing improvement

Talk to Sales

Where AI agents create business value

AI agents are strongest when the workflow has repeatable decisions, multiple systems, clear escalation paths, and enough volume to justify controlled automation.

01

Support and success operations

Retrieve account context, classify requests, draft replies, update tickets, check entitlement, and escalate unusual cases.

02

Finance and invoice workflows

Read invoices, validate totals, check vendor records, prepare approvals, route exceptions, and update finance systems with review.

03

Compliance and evidence collection

Collect evidence across systems, summarize policy gaps, prepare audit packets, and route high-risk decisions to human owners.

04

Revenue and CRM operations

Research accounts, update CRM fields, prepare follow-ups, enrich records, check rules, and keep approval steps visible.

Security, IP, and operational control

Agents can touch sensitive systems and perform real actions. Security is not just prompt filtering; it is the control model around tools, credentials, approvals, logs, and ownership.

01

Scoped credentials

Agents receive only the tool access needed for the workflow. Sensitive credentials remain controlled by your systems and access policies.

02

Approval before risky action

High-impact actions can require human approval with the exact payload, source context, and expected system change visible before commit.

03

Audit trails and replay

Prompts, retrieved context, tool calls, API responses, approval decisions, errors, and final outcomes are logged for review.

04

Client-owned implementation

Workflow code, tool contracts, prompts, docs, runbooks, and implementation artifacts are prepared for your ownership under the engagement terms.

What should change after an AI agents and workflow automation engagement

Agent automation should remove repeatable work without creating an unreliable black box. Buyers need to know which tasks are safe to automate, which need human approval, which systems the agent can touch, and how success will be measured after launch.

The workflow is mapped before the agent is built

We document triggers, inputs, approvals, tools, permissions, exceptions, and fallback paths before implementation. That prevents the team from building a clever agent that cannot operate inside the real business process.

The agent has measurable task quality

Useful agents need evals for task completion, tool-use accuracy, escalation quality, latency, cost, and failure handling. The system should show where the agent is reliable and where human review still protects the business.

Automation can be governed after launch

The engagement should leave behind prompt or policy versions, tool access rules, audit logs, escalation ownership, monitoring, and a process for improving the agent without breaking production workflows.

Scope an agent workflow before you automate it

Share the workflow, tools, systems, approvals, and failure modes you want to improve. We will help you decide what the agent should own, what humans should approve, and what needs to be instrumented before launch.

Scope an agent workflow Compare AI pods

hello@devlyn.ai

NDA support

Tool access review

Human approval design

Traceable handover

Frequently Asked Questions

Direct answers for teams comparing AI agent development, workflow automation, RPA, and internal automation builds.

The service includes workflow mapping, agent architecture, tool contracts, integrations, human approval design, state handling, retries, evaluation, observability, security controls, deployment support, and handover documentation.

A chatbot mainly responds to users. An AI agent can retrieve context, reason over a task, call tools, prepare or execute actions, request approval, and update systems within defined boundaries.

Yes. Agents can connect to CRMs, ERPs, ticketing systems, finance tools, document systems, internal APIs, and workflow platforms when access, permissions, and audit requirements are clear.

We define tool scopes, validation rules, action boundaries, approval gates, credential handling, logging, and blocked-action behavior before production actions are enabled.

Many production agents should include human approval for high-risk actions. The right level of oversight depends on the workflow, user impact, data sensitivity, and cost of a wrong action.

Good candidates have repeatable steps, clear data sources, known exceptions, measurable outcomes, and a human owner for escalations. Poor candidates are vague, high-risk, or dependent on judgment no one can evaluate.

Sometimes. AI agents can improve workflows that require language understanding, flexible context, or decision preparation. Deterministic RPA may still be better for simple repetitive UI actions with stable rules.

We test normal task paths, edge cases, failed tools, bad inputs, prompt injection attempts, approval flows, duplicate actions, incomplete data, cost behavior, and trace evidence.

Yes. We can design MCP-style tool surfaces, internal APIs, and tool gateways, but tool access must be governed with schemas, permissions, authorization, and monitoring.

Your organization owns the implementation artifacts according to the engagement terms. This can include workflow code, prompts, tool contracts, documentation, runbooks, and evaluation assets.

Yes. We can audit an existing prototype for tool risk, prompt behavior, state handling, integrations, eval coverage, observability, security, and handover readiness.

Choose an assessment when workflow risk is unclear, a single workflow build when one process is ready, and an agent workflow program when multiple departments or tool surfaces need shared governance.

Yes. Support can include trace review, tool updates, prompt changes, failure analysis, cost review, approval tuning, security retesting, and new workflow onboarding.

We can begin once the workflow owner, target systems, access expectations, risk boundaries, and commercial terms are clear. The timeline depends on integration depth, data sensitivity, and required approval controls.