Cline Agentic Coding Workflow: Plan/Act, MCP, and Human Approval in Outsourced Delivery
A Cline AI coding agent workflow is safe enough for outsourced delivery only when the client and service provider convert its Plan/Act modes, tool permissions, MCP connections, and approval prompts into explicit delivery gates. Cline can read and change files, execute terminal commands, use browser capabilities, and connect to external tools; those capabilities create leverage, but they also move part of the delivery process from human execution to machine-proposed action. The client therefore needs evidence of who approved what, which systems the agent could reach, what tests ran, and how a change could be reversed not a promise that “a human was in the loop.” [1]
This guide treats Cline as an operating component inside an outsourced engineering system. It explains a gated Plan/Act workflow, where MCP expands the trust boundary, why repeated approval can become weak oversight, and which controls a CTO, Head of Engineering, Product Owner, or vendor manager should require before allowing agent-assisted changes into a client repository.
Key Takeaways
- Use Plan mode as a contractual scope gate, not merely a thinking mode. Plan mode can inspect and discuss the codebase but cannot modify files or execute commands; the approved plan should define scope, constraints, acceptance evidence, and stop conditions before Act mode begins. [2]
- Approve capabilities by risk tier. Read/search actions may be eligible for limited auto-approval; edits, commands, browser actions, MCP tools, secrets, infrastructure, and release operations should require progressively stronger controls. Cline evaluates Auto Approve per tool call. [3]
- Treat every MCP server as a new trust boundary. Maintain an approved server registry, named owner, tool inventory, least-privilege scopes, credential path, allowed environments, and revocation procedure.
- Separate action approval from outcome acceptance. An operator may authorize a tool call, but an independent reviewer should accept the resulting code based on diff, tests, security checks, and traceable requirements.
- Pilot the control system as well as the tool. Measure accepted lead time, review effort, rework, escaped defects, approval overrides, MCP calls, and tool cost against a comparable baseline; do not infer a universal productivity ratio.
Why a Cline workflow is easy to get wrong in outsourcing
- Approval can be present but meaningless. A developer may approve many small actions without validating the cumulative design, test coverage, or production impact.
- The visible editor workflow may not be the real execution path. A provider can also use Cline through CLI or headless automation, where the official command reference currently states that prompt runs default to Act mode with auto-approve enabled. [4]
- MCP changes more than context quality. An MCP server can expose tools that reach issue trackers, databases, cloud platforms, documentation systems, or other APIs. Each connection therefore adds permissions and external side effects that need their own owner and approval policy. [5]
- Rollback is not acceptance. Checkpoints can restore project files after a tool action, but they do not prove the change was correct, secure, licensed appropriately, or aligned with the client’s business intent. [9]
- Outsourcing adds a second accountability boundary. The client owns business and risk decisions, while the provider operates the development workflow. Without named decision rights, both parties can assume the other validated the agent’s output.
What the Cline AI coding agent changes in outsourced delivery
Cline’s core workflow is not simply code generation. It can gather repository context, propose a plan, edit files, execute commands, inspect results, and call external tools. In an internal team, these actions may sit inside one engineer’s normal authority. In an outsourced team, they cross contractual, access, and acceptance boundaries. The practical question is therefore not whether Cline can perform an action; it is whether the action is permitted, attributable, reviewable, and reversible in the client’s delivery system.
| Cline capability | Delivery change | Evidence the client should require |
|---|---|---|
Repository reading and search |
The agent can assemble context faster, but may also read files outside the intended task boundary. | Approved workspace, data classification, .clineignore, allowed repositories, and a record of excluded sensitive paths. [8] |
File editing |
Implementation can move from suggestion to direct repository change. | Bounded task, branch protection, diff, author/operator identity, tests, reviewer, and acceptance criteria. |
Terminal commands |
The agent can build, test, install packages, alter local state, or invoke scripts. | Command allow/deny policy, isolated environment, dependency controls, captured output, and escalation for destructive or networked commands. |
MCP tools |
The agent can move beyond the repository into external systems and APIs. | Approved server and tool list, scopes, credential owner, data flow, per-tool approval rule, logs, and revocation test. |
Checkpoints |
Project-file changes can be compared or restored after tool use. | Checkpoint status, normal Git branch and commits, rollback owner, and separate validation for external side effects. |
A gated Cline Plan/Act workflow for outsourced delivery
Cline separates planning from execution. In Plan mode, it can read the codebase, search, and discuss strategy, but it cannot modify files or execute commands. Switching to Act mode retains the conversation context and allows implementation actions. [2] That separation becomes useful governance only when the engagement turns it into named gates with required artifacts.
1. Task intake
Define business outcome, repository, allowed environment, risk class, excluded data, and acceptance owner.
2. Plan-mode analysis
Cline inspects relevant context and proposes files, approach, dependencies, tests, assumptions, and unresolved questions.
3. Human plan approval
A named technical owner accepts or revises the plan. High-risk items obtain client security, architecture, or product approval before execution.
4. Controlled Act execution
The operator switches to Act mode with only the permissions required for the task. Edits and commands run in an isolated branch or workspace.
5. Evidence generation
The provider captures the final diff, commands, test results, static/security checks, MCP calls, assumptions, and unresolved risks.
6. Independent review
A reviewer who did not merely operate the agent evaluates the code and evidence against the approved plan and acceptance criteria.
7. Outcome acceptance or rollback
The authorized client or provider owner accepts the increment, requests rework, or restores the relevant state. Production release remains a separate controlled action.
| Gate | Minimum artifact | Decision owner | Stop condition |
|---|---|---|---|
| Task intake | Task card with outcome, constraints, risk class, acceptance criteria | Product or engineering owner | Scope, authority, or data boundary is unclear |
| Plan approval | Affected files/components, approach, test plan, assumptions, MCP/tool needs | Vendor tech lead; client architect for material design changes | Plan exceeds task or needs unapproved access |
| Act authorization | Permission profile, branch/workspace, command and MCP policy | Agent operator within policy; lead for exceptions | Unexpected command, external call, secret, or environment |
| Review and acceptance | Diff, test/security evidence, traceability, limitations, reviewer decision | Independent reviewer and acceptance owner | Evidence is missing or acceptance criteria fail |
Use Plan mode as a scope and reasoning gate
The strongest use of Cline Plan Act in outsourced delivery is to make hidden assumptions visible before code changes begin. The plan should not be a broad narrative such as “implement the feature and add tests.” It should be reviewable at the level needed to detect scope expansion, architectural drift, unsafe access, and untestable acceptance.
Require the plan to answer eight questions
- What business outcome and acceptance criteria does the task serve?
- Which repository, modules, files, interfaces, and data paths are expected to change?
- Which areas are explicitly out of scope?
- What assumptions require confirmation before implementation?
- Which commands, dependencies, browser actions, or MCP tools are expected?
- Which tests and security checks will demonstrate completion?
- What could create irreversible external effects?
- Who can approve a plan exception, and when must the agent stop?
Project-specific Cline Rules
Cline can make coding standards, architecture constraints, test expectations, and reusable instructions persistent across conversations; workspace rules can also be stored with the project so the team shares the same baseline. [7] Rules are useful context, but they are not policy enforcement by themselves. The provider should show that the applicable rules were loaded, versioned, reviewed, and consistent with the client’s current standards.
Control Act mode by capability, environment, and consequence
Act mode is where the agentic coding workflow becomes operational. A practical permission design should distinguish the ability to observe, modify, execute, connect, and release. Cline’s Auto Approve settings are evaluated for each tool call, and the official guidance recommends starting with read access while leaving edits, commands, browser actions, and MCP use subject to approval until there is a clear reason to relax them. [3]
| Risk tier | Typical actions | Recommended approval pattern | Required evidence |
|---|---|---|---|
Tier 1: Observe |
Read approved files, search code, inspect local documentation | Limited auto-approval may be acceptable inside an approved workspace | Workspace and exclusion rules; no sensitive paths |
Tier 2: Modify |
Edit source, tests, configuration, documentation | Per-task authorization in a branch; no direct protected-branch write | Diff, checkpoint/commit, tests, reviewer |
Tier 3: Execute |
Build, test, package, dependency install, local migration | Command allowlist or bounded approval; isolated environment | Command log, exit status, changed files, dependency record |
Tier 4: Connect |
MCP calls, browser/API access, issue updates, database queries | Approved server/tool and least-privilege credential; confirmation for writes | Tool, arguments, result, data class, external side effect |
Tier 5: Release or administer |
Production deployment, infrastructure mutation, credential or access changes | Do not delegate to general agent auto-approval; use existing privileged change controls | Change ticket, segregation of duties, release evidence, rollback plan |
CLI/headless warning: do not assume the editor’s confirmation prompts govern every provider workflow. As of this review, Cline’s official CLI reference says a prompt defaults to Act mode with auto-approve enabled. [4] An outsourcing agreement should therefore require disclosure of the actual interface and flags used, prohibit unattended write execution unless separately approved, and define command permissions, branch isolation, logs, and review for any automated run.
Where MCP expands the trust boundary
MCP allows Cline to connect to external APIs and services through local or remote servers. Cline’s configuration supports server-specific tool exposure and an autoApprove list, while its documentation recommends using trusted servers, keeping secrets in environment variables, limiting auto-approval, and reviewing tool calls. [5] The MCP tools specification also states that a human should be able to deny tool invocations and that applications should make exposed tools and invocations visible and present confirmation prompts. [6]
For an outsourced team, an MCP server is not “just another plugin.” It is a delegated capability. Its risk depends on the specific tools, credentials, data, and environments it can reach.
| MCP use | Default decision | Conditions before approval |
|---|---|---|
Read approved product or API documentation |
Potentially allow for a pilot | Read-only tool, non-sensitive source, trusted server, logged calls |
Read issue tracker or CI status |
Allow only with scoped account | Project-limited token, no unrelated projects, data retention understood |
Create or update issues, comments, or pull-request metadata |
Require write confirmation initially | Clear author attribution, content review, reversible operation, rate limits |
Query client databases or production observability |
Deny by default | Separate approval, masking, read-only views, auditable identity, incident procedure |
Change cloud, deployment, identity, or production state |
Keep outside general Cline autonomy | Use established privileged change-management controls and segregation of duties |
The minimum MCP registry
For each server, record the business purpose, server source and version, deployment location, transport, owner, exposed tools, read/write behavior, credential owner, scopes, data classes, approved environments, auto-approved tools, logging destination, review date, revocation method, and emergency disable owner. A server that cannot be described at this level should not be connected to a client delivery workspace.
Human approval is a control point, not proof of quality
The phrase “human in the loop” can hide several different decisions: approving a plan, authorizing a tool call, reviewing a diff, accepting an outcome, or releasing to production. These decisions require different knowledge and authority. NIST guidance emphasizes defining and differentiating human roles and responsibilities for human-AI configurations and oversight. [10]
There is also an operational reason not to rely on repeated clicks. A June 2026 preprint analyzed 11,429 reviews by 400 repeat reviewers of AI-agent pull requests over seven months. Approval increased across reviewer-experience deciles while change requests declined; the authors interpret the combined pattern as consistent with habituation, while noting that observational data cannot establish causality. The study is not Cline-specific and covers open-source repositories, so it should be used as a review-risk signal rather than an enterprise benchmark. [12]
Human review became more permissive across AI-agent PR experience deciles
Approval rate, first decile: 27.9%
Approval rate, tenth decile: 42.4%
Change-request rate, first decile: 11.2%
Change-request rate, tenth decile: 5.6%
Source: Yu et al., 2026. Values pooled by within-reviewer experience decile. Preprint; not Cline-specific; observational evidence does not prove causality. [12]
| Review metric | First experience decile | Tenth experience decile | Observed change |
|---|---|---|---|
| Approval rate | 27.9% | 42.4% | +14.5 percentage points |
| Change-request rate | 11.2% | 5.6% | -5.6 percentage points |
Design approval to resist habituation
- Risk-tier the review. A formatting change should not receive the same review protocol as authentication, billing, data migration, or infrastructure code.
- Require an evidence bundle. The reviewer should receive the approved plan, final diff, commands, tests, security checks, MCP calls, and known limitations together.
- Separate operator and accepter for material changes. The person who repeatedly clicked action approvals should not be the only person accepting the result.
- Use explicit review questions. Ask which assumption is most likely wrong, what was not tested, what external state changed, and what rollback would not restore.
- Monitor review behavior. Track review time, comment depth, rework after approval, escaped defects, and exception rates; investigate trends rather than setting arbitrary universal thresholds.
Cline governance decision matrix for outsourced teams
The following matrix is an operating recommendation, not a vendor benchmark. It connects task consequence to mode, permission, review, and evidence so that autonomy increases only when the delivery system can absorb failure.
| Task class | Examples | Plan gate | Auto-approval | Review and environment |
|---|---|---|---|---|
Low consequence |
Documentation, test-data helpers, local developer scripts with no secrets | Brief plan or bounded task card | Read/search may be automatic; writes remain branch-scoped | Normal peer review; local or sandbox environment |
Moderate consequence |
Feature code, refactoring, tests, non-sensitive integration work | Approved affected-components and test plan | No blanket YOLO; bounded edits and commands under team policy | Independent PR review, CI, security checks, isolated integration environment |
High consequence |
Authentication, authorization, payments, regulated data, schema migration, core architecture | Client architecture/security approval with threat and rollback considerations | Per-action approval; tightly limited commands and MCP | Senior specialist review, targeted testing, security validation, non-production first |
Privileged or irreversible |
Production deployment, data deletion, IAM changes, credential rotation, destructive infrastructure operations | Formal change approval outside the agent session | Prohibited for general auto-approval | Existing privileged workflow, segregation of duties, monitored execution, tested rollback |
Evidence and control checklist before a vendor uses Cline
NIST’s Secure Software Development Framework recommends integrating secure practices into the software lifecycle and provides a common basis for communication between software producers and acquirers. [11] The checklist below translates that principle into a Cline-specific evidence request without turning this article into a broader security standard for autonomous agents.
| Control | Evidence to request | Red flag |
|---|---|---|
Approved usage scope |
Repositories, project phases, task classes, prohibited actions, named owners | “Developers can use it wherever helpful” |
Interface and execution mode |
IDE, TUI, CLI/headless use; Plan/Act defaults; flags; auto-approval profile | Provider describes editor prompts but cannot account for automation runs |
Model and data path |
Approved model/provider, account ownership, data handling, retention, region, sensitive-data rule | Personal keys or unapproved providers used on client code |
Context boundary |
.clineignore, workspace selection, secret exclusions, generated/dependency exclusions | Whole monorepo or home directory opened without review |
Persistent rules |
Versioned rules for architecture, code style, testing, secure patterns, prohibited behavior | Rules exist only in one developer’s local settings |
Command policy |
Allowed/denied command categories, network access, package install policy, sandbox | Unattended shell access with broad credentials |
MCP registry |
Servers, tools, versions, owners, scopes, credentials, auto-approval, logs, revocation | Ad hoc server installation by individual engineers |
Change isolation and recovery |
Branch protections, commits, checkpoint status, restore test, external-effect rollback | Checkpoint treated as a substitute for Git and environment rollback |
Verification and acceptance |
Required tests, code/security scans, traceability, independent reviewer, acceptance owner | “Cline completed the task successfully” used as acceptance evidence |
Observability and incident response |
Usage/cost logs, tool-call evidence, exception process, credential revocation, incident owner | No way to identify what agent action occurred after a defect or exposure |
For organizations that need centralized administration, Cline Enterprise documents capabilities such as SSO, role-based access control, model and tool controls, remote configuration, OpenTelemetry export, usage tracking, and selective administrative audit logging. [13] These capabilities may support governance, but the client should still verify the configured policy and retained evidence rather than treating the product tier as proof of control effectiveness.
Who approves what: a responsibility matrix
| Decision | Client product/engineering owner | Client security/platform owner | Vendor tech lead | Agent operator | Independent reviewer |
|---|---|---|---|---|---|
Business outcome and acceptance criteria |
Accountable | Consulted for risk | Responsible for technical translation | Informed | Informed |
Cline plan and task boundary |
Approves material product or architecture impact | Approves sensitive access or high-risk design | Accountable | Prepares | May challenge |
Tool call within approved profile |
Informed by policy | Owns privileged exceptions | Owns team policy | Responsible | Not the action approver |
Code and evidence review |
Accepts business outcome where required | Reviews security-critical changes | Ensures reviewer assignment | Provides evidence and responds | Responsible for review decision |
Production release |
Accountable per release policy | Accountable for privileged controls | Responsible as assigned | No independent release authority by default | Confirms review status |
How to pilot Cline without turning the client repository into an experiment
A useful Cline AI coding agent pilot should produce a decision about fit and controls, not a showcase task selected because it is easy to complete. Use comparable work, preserve a conventional fallback, and evaluate accepted outcomes after review and rework.
Stage 1: Control validation in a sandbox
- Confirm model/provider approval, workspace boundary, .clineignore, rules, checkpoints, command restrictions, and logging.
- Test denial, stop, restore, MCP revocation, and incident escalation not only the happy path.
- Use synthetic or non-sensitive data and no production credentials.
Stage 2: Bounded repository tasks
- Select repeatable, reviewable tasks with clear acceptance criteria and low external consequence.
- Require Plan approval, branch isolation, independent review, and the complete evidence bundle.
- Compare with similar recent tasks, not with an unrelated portfolio average.
Stage 3: Controlled MCP use
- Add one server and one business purpose at a time.
- Begin read-only, validate logs and scopes, then consider specific write tools.
- Keep infrastructure, identity, production data, and release actions outside the general pilot.
Stage 4: Scale by proven task class
- Expand only where accepted outcomes improve without unacceptable review, rework, security, or coordination cost.
- Standardize reusable rules, task templates, approval profiles, evidence capture, and reviewer guidance.
- Reassess when the codebase, model, Cline version, MCP server, provider team, or risk classification changes.
Metrics that support a real decision
| Metric | Definition | Why it matters |
|---|---|---|
Accepted lead time |
Task ready to acceptance, including review and rework | Avoids counting fast code generation as finished delivery |
Human review effort |
Operator, reviewer, security, and client acceptance time | Shows whether speed shifted cost downstream |
Rework after first review |
Effort or cycles required before acceptance | Tests context quality and implementation reliability |
Escaped defect or rollback |
Accepted change later corrected, reverted, or linked to an incident | Connects the workflow to actual delivery quality |
Approval exception rate |
Actions needing policy override or escalation | Reveals poorly bounded tasks or permission profiles |
Tool and model cost per accepted task |
Cline/model/API cost divided by accepted outcomes | Supports economic comparison without assuming a fixed productivity multiplier |
When Cline fits and when it does not
| Readiness factor | Good fit signal | Delay or restrict adoption when |
|---|---|---|
Task definition |
Outcomes, constraints, and acceptance tests can be stated | Work depends on unresolved product decisions or tacit stakeholder knowledge |
Repository condition |
Build and tests are reproducible; relevant context is available | The codebase is unbuildable, undocumented, or contains uncontrolled secrets |
Review capacity |
Qualified independent reviewers can inspect increased change volume | The team already treats review as a bottleneck or rubber stamp |
Access governance |
Least privilege, identity, secrets, MCP registry, and revocation are established | Personal accounts, shared credentials, or broad production access are normal |
Delivery accountability |
Client and provider decision rights are named and evidence-based | The contract measures activity while ownership of defects and acceptance remains unclear |
The Cline AI coding agent may be useful even when the full Act workflow is not ready. A team can begin with Plan-mode codebase exploration, test planning, documentation, or bounded read-only analysis while strengthening repository hygiene, review capacity, and access controls. The correct adoption path is the smallest useful capability that the delivery system can govern.
How Bestarion can help
Bestarion provides software development, testing, DevOps, maintenance, support, and staff augmentation services. [14] For a Cline-enabled engagement, the practical objective should be a governed delivery workflow rather than tool adoption in isolation.
- Define task classes, Plan/Act gates, decision rights, and evidence requirements with the client.
- Establish repository rules, context exclusions, command restrictions, MCP controls, review, testing, and rollback.
- Run a bounded pilot against comparable delivery work and report accepted outcome, review, rework, quality, risk, and tool-cost evidence.
- Scale only the task classes and permissions that demonstrate repeatable value under the agreed controls.
When evaluating an outsourced provider, ask for a walkthrough of one completed task from intake and Plan approval through Act execution, MCP activity, tests, review, acceptance, and any rollback. A live evidence chain is more informative than a feature list or an unsupported productivity claim.
FAQ
Can Cline change files in Plan mode?
No. Cline’s official documentation states that Plan mode can read the codebase, search, and discuss strategy but cannot modify files or execute commands. Changes begin after switching to Act mode. [2]
Does an MCP coding agent always ask for human approval?
Not automatically in every configuration. The MCP specification recommends a human-in-the-loop design with the ability to deny tool invocations, while Cline can configure MCP tools and auto-approval behavior. The outsourcing control must therefore verify the actual server, exposed tools, permissions, and approval settings rather than infer safety from the MCP label. [5] [6]
Are Cline checkpoints enough to make agentic changes safe?
No. Checkpoints can compare or restore project-file snapshots after tool use, which helps recovery. They do not reverse every external side effect, replace normal Git and release controls, or prove that code meets requirements and security expectations. [9]
Should an outsourced team use Cline YOLO or unattended headless mode?
Not as a general default for client repositories. Any unattended write workflow should be separately approved for a bounded task class, isolated environment, restricted commands and MCP tools, complete logs, independent review, and a tested recovery path. Cline’s current CLI defaults make this an explicit due-diligence question. [4]
What is the most important evidence before approving Cline use?
Before approving a Cline AI coding agent workflow, ask for one traceable completed task: the approved plan, actual permission profile, model and data path, changed files, commands, MCP calls, test/security evidence, independent review, acceptance decision, and rollback record if used. This shows whether the provider has a working control system rather than a policy statement.
What to Keep in Mind
- Plan before permission. Approve the intended change, constraints, evidence, and stop conditions before Act mode.
- Permission is not acceptance. Tool-call approval authorizes an action; independent review and named acceptance authorize the outcome.
- MCP is an access decision. Inventory every server, tool, credential, scope, environment, log, and revocation path.
- Verify the real execution path. IDE, CLI, headless runs, defaults, and auto-approval settings must all be in scope.
- Scale from evidence. Expand Cline use only where accepted lead time, review effort, rework, quality, risk, and cost support the decision.
References
- Cline, “Cline Overview,” Cline Docs. Accessed: Jul. 15, 2026. [Online]. Available: https://docs.cline.bot/cline-overview
- Cline, “Plan & Act Mode,” Cline Docs. Accessed: Jul. 15, 2026. [Online]. Available: https://docs.cline.bot/core-workflows/plan-and-act
- Cline, “Auto Approve & YOLO Mode,” Cline Docs. Accessed: Jul. 15, 2026. [Online]. Available: https://docs.cline.bot/features/auto-approve
- Cline, “CLI Reference,” Cline Docs. Accessed: Jul. 15, 2026. [Online]. Available: https://docs.cline.bot/cli/cli-reference
- Cline, “MCP Overview,” Cline Docs. Accessed: Jul. 15, 2026. [Online]. Available: https://docs.cline.bot/mcp/mcp-overview
- Model Context Protocol, “Tools,” MCP Specification, Jun. 18, 2025. Accessed: Jul. 15, 2026. [Online]. Available: https://modelcontextprotocol.io/specification/2025-06-18/server/tools
- Cline, “Rules,” Cline Docs. Accessed: Jul. 15, 2026. [Online]. Available: https://docs.cline.bot/customization/cline-rules
- Cline, “.clineignore,” Cline Docs. Accessed: Jul. 15, 2026. [Online]. Available: https://docs.cline.bot/customization/clineignore
- Cline, “Checkpoints,” Cline Docs. Accessed: Jul. 15, 2026. [Online]. Available: https://docs.cline.bot/core-workflows/checkpoints
- National Institute of Standards and Technology, “Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile,” NIST AI 600-1, Jul. 2024. Accessed: Jul. 15, 2026. [Online]. Available: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf
- M. Souppaya, K. Scarfone, and D. Dodson, “Secure Software Development Framework (SSDF) Version 1.1,” NIST SP 800-218, Feb. 2022. Accessed: Jul. 15, 2026. [Online]. Available: https://csrc.nist.gov/pubs/sp/800/218/final
- H. Yu, L. Liu, X. Jiang, Y. Jia, S. Wang, P. Qian, and Y. Chen, “Habituation at the Gate: Rising Approval and Declining Scrutiny in Human Review of AI Agent Code,” arXiv:2606.22721, Jun. 2026. Accessed: Jul. 15, 2026. [Online]. Available: https://arxiv.org/abs/2606.22721
- Cline, “Cline Enterprise,” Cline Docs. Accessed: Jul. 15, 2026. [Online]. Available: https://docs.cline.bot/enterprise-solutions/overview
- Bestarion, “Software Development,” Bestarion. Accessed: Jul. 15, 2026. [Online]. Available: https://bestarion.com/us/services/software-development/
