AI-Adjacent Tools: What's Worth the Complexity

I recently tried the Model Context Protocol, Anthropic’s attempt to standardise how AI systems connect to data sources. The concept is compelling: a universal protocol that works like USB-C for AI applications. Industry adoption came quickly, with OpenAI and Google confirming support, and developers creating over a thousand MCP servers within weeks of launch.

In practice, I found I didn’t need it. Claude Code handles everything I require through standard CLI tools: gh for GitHub operations, aws for infrastructure, curl for API testing, and the usual database CLIs like psql and mongo. There was no special protocol to configure, no integration layer to maintain. Just the tools I already knew working as they always had.

On the same project, however, observability tools proved essential. Our AI-powered feature launched successfully, but by week two costs had exploded to ten times our estimates. Without proper monitoring, we would have discovered this through a budget alert. Instead, the observability platform showed us precisely which prompts were expensive, which model calls were redundant, and where we could optimise. The subscription paid for itself almost immediately.

This creates an interesting paradox. Some tools receive enormous attention whilst solving problems you may not have. Others, less discussed, turn out to be genuinely essential. The challenge lies in distinguishing between them before committing time and budget to the wrong choices.

The AI tooling landscape has become remarkably crowded. Orchestration platforms, specialised IDEs, context protocols, prompt management systems, workflow automation, observability stacks. Everyone promises seamless AI integration, but few ask whether you actually need these tools in the first place.

Engineering leaders face a difficult evaluation problem. Marketing promises transformation. Free trials demonstrate polished workflows. But the critical questions remain: will this integrate with how you actually work? Does the value justify the learning curve, subscription costs, and ongoing maintenance? Will the company exist in two years, or is this another VC-funded experiment likely to pivot or shut down?

What follows is what I’ve learned from using these tools in production environments. This isn’t a dismissal of modern tooling, nor is it uncritical enthusiasm. It’s an honest assessment of what works and what doesn’t, based on actual experience rather than marketing promises.

We’ll examine three categories: simple approaches that should form your foundation, specialised platforms that solve real problems when you have them, and emerging technologies worth watching but not rushing to adopt. The underlying principle is straightforward. Start simple, add complexity only when simpler approaches demonstrably fail, and measure actual value rather than promised potential. Your ability to think systematically about problems matters far more than your tool collection.

The Simple Stack (Start Here)

Before considering specialised tools, establish a solid foundation using simple, proven approaches. These aren’t temporary workarounds you’ll replace with “proper” infrastructure later. For most individual developers and small teams, they remain the right solution indefinitely.

Boring tools work remarkably well. Sophisticated platforms offer impressive feature lists, but boring tools integrate seamlessly with existing workflows, require no learning curve, and create no vendor lock-in. When simple approaches handle your needs effectively, additional complexity becomes overhead rather than value.

Markdown Files for Context

My context management system is straightforward: markdown files in the project repository.

• PROJECT_STATE.md tracks current goals, recent decisions, next steps, and open questions
• DECISIONS.md logs architectural and implementation choices with brief rationale
• Session handoff files capture mental state between work periods

These are plain text files that any AI tool can read, version controlled alongside your code, and human readable when you need to understand project history. No specialised platform, no subscription, no proprietary format creating lock-in.

When starting a Claude Code session, I simply reference these files:

Read PROJECT_STATE.md and tell me what we should focus on today.

Context loads in seconds. Work begins immediately rather than after lengthy explanations about what we were working on last time.

I’ve written about this approach in detail in Helping AI Agents Remember. The essential points: simple files, version controlled, human readable, no lock-in.

When to upgrade: This approach scales effectively for individuals and small teams. Beyond approximately 50 people, or when managing complex cross-system context, you might require something more sophisticated. However, most teams assume they need specialised knowledge management far earlier than they actually do.

Git for Decision Tracking

Git already provides sophisticated state management, decision history, and collaboration infrastructure. Since you’re using it anyway, leverage what it offers:

• Commit messages document what changed and why
• Tags mark significant milestones
• Branch names indicate current work
• Pull request descriptions explain reasoning

When Claude Code asks about previous architectural decisions, I point to specific commits. When understanding why an approach changed, git log and git blame provide the history with timestamps and context.

I explored this thoroughly in Documentation as Decision History. Using Git for decision tracking isn’t a clever workaround. It’s leveraging infrastructure you already maintain.

When to upgrade: The only scenario requiring more would be structured querying by category or automated analysis of architectural evolution. In that case, Git’s text-based format might become limiting. Most projects never reach that threshold. Git’s capabilities exceed what most work requires.

CLI Tools and Shell Scripts

The Model Context Protocol represents an interesting case study. It promises standardised connections between AI and data sources. This is conceptually appealing for platform builders creating reusable AI infrastructure.

For individual developers, however, Claude Code already handles everything required through standard CLI tools:

• gh for GitHub operations (pull requests, issues, workflows, releases)
• aws for infrastructure management
• curl for API testing and integration
• psql, mysql, mongo for database queries, schema changes, and data operations

These tools offer several advantages:

• Composable: Output from one feeds into another
• Transparent: You can observe what’s happening
• Debuggable: Error messages are clear
• Standard: Your entire team already knows them

You’re not waiting for someone to build an MCP server for your specific data source. You’re simply using tools that already exist and work reliably.

When to upgrade: Shell scripts only become problematic when coordinating complex operations across five or more services with intricate dependencies and error handling requirements. At that point, workflow orchestration platforms might provide value. However, this threshold is considerably higher than most developers assume. Scripts scale more effectively than their reputation suggests.

Your Development Environment

Whatever IDE you’re using, whether with Dev Containers, custom configurations, or any other setup, you already have functioning infrastructure that supports effective development.

AI-native IDEs like Cursor and Windsurf warrant attention. IBM’s Claude IDE demonstrated 45% productivity improvements among 6,000 early users. That’s measured data from actual use, not marketing claims. Cursor has earned trust from more than half the Fortune 500. These aren’t marginal improvements. They’re substantial productivity shifts for teams that benefit from them.

However, your current environment works. You understand its capabilities and limitations. It integrates with your actual workflow. Switching introduces learning curves, workflow disruption, additional subscriptions, and uncertainty about whether claimed benefits will materialise in your specific context.

My personal experience reflects this tension. Claude Code works well in my standard development environment. I haven’t hit limitations that an AI-native IDE would solve. But I’m watching this space because the data from teams using these tools is compelling. The 45% productivity gain IBM reports isn’t trivial.

When to upgrade: If you’re curious, conduct a proper trial. Use it for two weeks on real work, not toy examples. Measure concrete changes in task completion time, friction points encountered, and revision cycles required. If gains are marginal, your current setup remains sufficient. Let actual experience drive the decision, not assumptions about what you should be using.

The simple stack in practice:

• Markdown files for context (PROJECT_STATE.md, DECISIONS.md)
• Git for history and state tracking
• CLI tools for actions (gh, aws, curl, psql, mongo)
• Your current development environment

No specialised platforms, no additional subscriptions. Just tools you already know, working together effectively.

This doesn’t mean specialised tools have no place. It means the burden of proof rests with specialised tools to demonstrate value beyond simple approaches, which brings us to examining when that burden is met.

When Specialised Tools Actually Matter

Not all specialised tools are hype masquerading as necessity. Some solve real problems that simple approaches genuinely can’t handle effectively. The key is distinguishing between tools that address actual pain points and tools that promise theoretical benefits.

This section examines three categories where specialised tools often provide genuine value: production observability, workflow orchestration, and enterprise knowledge management. For each category, we’ll explore the real problems they solve, when simple approaches suffice, and how to decide whether you need specialised platforms.

Production Observability

Tools like Langfuse, Agenta, Arize, and Coralogix represent one category where specialised platforms are often essential rather than optional.

The distinction between development and production use cases is critical. During development, logs and tests often suffice. You’re iterating quickly, working at small scale, directly observing AI behaviour. Production is fundamentally different. High volume, distributed systems, indirect observation, cost at scale, and quality requirements that matter to customers.

When you ship AI features to customers, observability becomes non-negotiable. You need to monitor several critical aspects:

• Quality: Are responses maintaining acceptable standards over time?
• Costs: Which prompts are expensive? Which model calls are redundant?
• Debugging: Why did this request fail? What patterns lead to poor responses?
• Optimisation: Where can we improve without degrading quality?

My experience illustrates this necessity. Our AI-powered feature launched successfully. Week one looked excellent. Week two, costs had exploded to ten times our estimates. Without proper monitoring, we would have discovered this through an angry budget alert. Instead, the observability platform showed us precisely which prompts were burning money, which model calls were redundant, and where we could optimise. We fixed the problem before costs spiralled out of control.

The tools provide aggregation, analysis, and alerting at scale that’s difficult to build yourself and tedious to maintain. Specialised platforms handle it well.

When you need it: Customer-facing AI features in production, or high-volume internal tools where costs matter significantly.

When you don’t: Personal productivity use cases, small team experimentation, or internal tools with low volume.

Open source options like Langfuse and Agenta reduce adoption risk. You’re not locked into proprietary platforms. Self-hosting keeps data internal. Start with fundamental metrics: cost per interaction, quality trends over time, error rates.

Workflow Orchestration

Orchestration platforms like n8n, Make, Zapier AI, and CrewAI solve a specific, valuable problem. They connect AI actions across multiple business systems in maintainable ways.

The problem becomes clear when you try to automate complex processes. Consider customer support: ticket arrives, AI categorises it, routes to appropriate team, suggests response from knowledge base, updates CRM, notifies assigned engineer. Building this with shell scripts is possible but brittle. Error handling becomes complex. State management gets messy. Team members who didn’t write the scripts struggle to maintain them.

Orchestration platforms provide several capabilities that address these pain points:

• Visual workflow builders that make logic clear
• Built-in error handling and retry logic
• State management across steps
• Extensive pre-built integrations with common services
• Team collaboration features

For organisations running multiple complex workflows, these capabilities justify platform adoption. The key question is whether you have workflows that justify orchestration. Not theoretical workflows you might build, but actual repeatable processes that are currently manual or inefficiently automated.

When you need it: Connecting five or more tools in multi-step processes with conditional logic, writing increasingly complex shell scripts that team members struggle to maintain, or managing multiple repeatable workflows.

When you don’t: Occasional one-off integrations (just write a script), workflows you can’t clearly define (understand the process first), or fewer than three systems involved.

Recommended starting point: n8n, which is open source, self-hosted, and genuinely extensible. This matters because you’re building infrastructure that might run for years. Start with one proven workflow. Something currently manual that’s been repeated enough times to understand its patterns. Measure time saved, error reduction, and team adoption before expanding.

Enterprise Knowledge Management

AI-powered knowledge management tools like Guru, Document360, Tanka, and Microsoft Viva Topics solve a specific problem. They help find information across large, distributed organisations.

The problem manifests at scale. Small teams with good documentation practices and disciplined maintenance can manage knowledge effectively with markdown files, wikis, and well-organised repositories. As organisations grow beyond about 50 people, information discovery becomes increasingly difficult. Knowledge lives in multiple systems: wikis, Confluence, code comments, tickets, chat history, email threads. Finding the right information consumes significant time.

AI-powered knowledge management addresses this through intelligent search that understands context and intent, in-workflow delivery that surfaces information without leaving your tools, and automatic organisation that reduces manual categorisation overhead.

The critical prerequisite: Fix documentation architecture first. This isn’t obvious but it’s essential. AI-powered search amplifies good documentation. It cannot rescue bad documentation. If your underlying information is poorly organised, inconsistent, outdated, or incomplete, AI search just helps people find bad information faster.

Before adopting knowledge management platforms, implement solid documentation architecture with canonical documentation and clear navigation, single sources of truth for specific domains, consistent structure across documentation, and systematic maintenance practices.

Once your documentation architecture is solid, AI-powered search multiplies its value. I’ve written about this in The Compass Pattern. Get the navigation right first, then add AI capabilities that make good practices even better.

The scaling threshold: Up to approximately 50 people with good documentation practices, simple approaches suffice (markdown files in Git, well-organised wikis, clear navigation). Beyond 100 people, especially across multiple teams and locations, specialised tools often provide value.

When you need it: 100+ people across multiple teams or locations, complex domains with rapidly changing information, multiple systems where knowledge lives, and time wasted searching that justifies investment.

When you don’t: Small teams with well-organised documentation, documentation architecture needs fixing first, or information doesn’t exist or is poor quality.

The Pattern

Specialised tools matter when simple approaches demonstrably fail. Not when they might fail, or when you imagine they’ll fail, but when you’ve tried simple approaches and hit their limits. Start simple, measure carefully, upgrade based on evidence.

The Emerging Tools (Watch, Don’t Rush)

Some tools generate significant hype whilst demonstrating unclear practical value. Watch closely whilst avoiding premature commitment.

Model Context Protocol

Anthropic’s MCP launched in November 2024 to standardise AI data connections. OpenAI and Google adopted it. Developers created over 1,000 MCP servers within weeks.

The promise: standard interfaces reduce integration overhead and improve interoperability.

A year later, the practical benefit for individual developers remains questionable. My experience: Claude Code already handled data access through CLI tools (gh, aws, curl, psql, mongo). Adding MCP meant configuring servers and maintaining integration layers to access data I could already reach. After a year of ecosystem development, this hasn’t fundamentally changed.

Value varies by use case:

• Platform builders: Creating AI products for customers benefits from standardised protocols
• Individual developers: CLI tools often simpler than specialised protocols
• Teams with stable data sources: Direct CLI access is simpler

Who needs it: Platform builders, organisations developing reusable AI infrastructure, teams with complex frequently-changing data sources.

Who doesn’t: Individual developers where CLI works, small teams, projects where protocol layers create problems.

Recommendation: The ecosystem has had a year to mature. If it hasn’t solved your problems yet, it might not be the right solution for your use case. Continue monitoring, but don’t feel pressure to adopt until there’s clear value for your specific context.

Prompt Management Platforms

Platforms like PromptLayer, Helicone, LangSmith, and Maxim manage, version, and optimise prompts systematically.

Simple approach: Git and markdown files. Version control prompts like code. This works initially.

Platform value: Platforms handle non-deterministic outputs. Prompts generate variable results. A/B testing and performance comparison across iterations become tedious without specialised tools.

McKinsey reports organisations frequently refining prompts see 20% accuracy increases. Substantial gains from systematic optimisation.

When you need it: 50+ prompts across multiple teams, customer-facing features where quality impacts users, systematic A/B testing requirements.

When simple suffices: Fewer than 20 prompts, internal tools with acceptable quality variation, no systematic optimisation.

Recommendation: Start with Git. Upgrade when version control becomes unwieldy or you need professional optimisation infrastructure.

The Decision Framework

Before adopting tools, work through five questions systematically.

Question 1: What Specific Problem Am I Solving?

Be specific and quantifiable. “We spend two hours daily re-explaining context” is specific. “Context could be better” is vague.

The red flag: If you can’t articulate a specific, measurable problem, you don’t need it yet.

Question 2: Have I Exhausted Simple Approaches?

Try simple solutions before adopting sophisticated platforms. The order matters:

• Markdown and Git before knowledge management platforms
• CLI tools and scripts before orchestration systems
• Logs and tests before observability infrastructure
• Standard development environments before AI-native IDEs

The honest assessment: have you actually tried the simple approach and found it insufficient, or are you assuming you need something sophisticated?

Most teams under-utilise their existing tools before adding new ones. Markdown files work remarkably well for context management. Git provides sophisticated state tracking. CLI tools compose powerfully. Your current IDE likely has capabilities you haven’t explored.

Simple approaches scale further than their reputation suggests. Git handles decision tracking for teams of dozens effectively. Shell scripts manage complex workflows longer than developers expect. Markdown documentation serves organisations far larger than you’d imagine.

Try simple first, not as a temporary workaround, but as a genuine solution. If simple approaches fail demonstrably, characterised by specific, repeated pain points you can describe, then evaluate specialised tools. But demand evidence of failure before assuming you need complexity.

Question 3: What’s the True Cost?

Account for learning curves, maintenance overhead, lock-in risks, and opportunity cost beyond subscription pricing.

Can you export data? Are integrations based on open standards? What happens if the company shuts down?

Apply the 3x rule: Value should exceed costs by factor of three. Benefits are overestimated whilst costs are underestimated. If expected value is marginal, stick with what you have.

Question 4: What’s My Exit Strategy?

Can you export data in standard formats? Are integrations based on open standards? What happens if the company ceases operations?

Favour open source options, standard protocols, and established companies with clear business models. Avoid proprietary platforms with uncertain futures and VC funded experiments without paths to profitability.

Preserve optionality. Assess risks honestly.

Question 5: Does This Integrate With How I Actually Work?

The MCP lesson is instructive. Theoretically powerful, yet practically redundant when Claude Code can use gh, aws, curl, and database CLIs directly. Tool value depends on workflow fit, not feature lists.

Test through real usage, not demos. Free trials on actual work reveal integration friction that polished demonstrations hide. Use tools for at least two weeks to move past novelty into routine. Initial enthusiasm often reflects newness rather than genuine improvement. Sustained benefit becomes apparent after tools become familiar.

Measure actual impact:

• How long do tasks take?
• How often do you hit friction?
• How many revision cycles do you need?

Compare these metrics between current workflows and trial tools. Improvement should be obvious and measurable, not subjective and debatable.

The red flag: If you’re changing your workflow to fit the tool rather than the tool fitting your workflow, reconsider. Good tools enhance how you work. Problematic tools require working differently to accommodate their requirements. Unless benefits are overwhelming, tools demanding workflow changes create ongoing friction.

This framework transforms tool evaluation from subjective preferences into objective assessment. You’re not guessing whether tools might help. You’re measuring whether they address specific problems better than alternatives whilst providing value exceeding their true costs.

Apply this systematically before adopting AI-adjacent tools. Most tools will fail one or more criteria, revealing they’re not right for your context currently. Some tools will pass all five, indicating genuine value. The framework saves you from both types of mistakes: adopting tools you don’t need and missing tools you do.

What’s Actually Coming

Three trends backed by clear evidence:

Trend 1: AI Agents Using Standard Tools Better

Claude Code already uses Git, gh, aws, curl, and database CLIs effectively. Agents are getting better at using existing infrastructure rather than requiring specialised integration layers.

Strategy: Document CLI tools and APIs thoroughly. Structure infrastructure for programmatic access. These investments benefit both humans and AI agents whilst avoiding dependencies on emerging protocols.

Trend 2: Existing Tools Adding AI Features

Your IDE is adding AI assistance through extensions. Git interfaces are incorporating AI analysis. Monitoring tools are implementing anomaly detection. Your current stack evolves rather than getting replaced.

Strategy: Let tools come to you. Evaluate AI features in existing tools before seeking alternatives. Upgrading existing subscriptions is often simpler than adding new tools.

Trend 3: Consolidation in the Tooling Space

Too many tools exist. The market will consolidate through mergers, acquisitions, and shutdowns. Standards will emerge from chaos.

Strategy: Favour open standards and established platforms. Be cautious with specialised tools from uncertain companies. Let markets mature before committing. Later followers often win by avoiding early adopters’ dead ends.

The pattern: convergence towards simplicity. AI agents handling standard tools reduces protocol proliferation. Existing tools adding AI reduces platform proliferation. Market consolidation reduces vendor proliferation.

Don’t over invest in transient infrastructure. Build on foundations likely to persist. In rapidly evolving spaces, patience wins.

Practical Recommendations by Team Size

Solo / Small Team (1-5 people)

Start with: Markdown files, Git, CLI tools, current IDE.

Consider: AI-native IDEs (two-week trial), basic observability only if shipping AI features to customers.

Skip: Knowledge management, orchestration, prompt management, MCP.

Growing Team (5-20 people)

Add: Basic observability if shipping AI features, solid documentation architecture.

Consider: Workflow orchestration for one proven process, AI-native IDEs if productivity data shows clear gains.

Watch: MCP ecosystem, prompt management as you approach 30-50 prompts.

Established Team (20-100 people)

Add: Knowledge management if documentation architecture is solid, observability for production AI becomes non-negotiable.

Consider: Prompt management for 50+ prompts, AI-native environments based on measured ROI, workflow orchestration for multiple proven cases.

Enterprise (100+ people)

Required: Observability, knowledge management, workflow orchestration.

Strongly consider: Prompt management platforms, enterprise IDE solutions, MCP infrastructure.

Strategic priorities: Build on standards, create evaluation processes, focus on integration and interoperability.

The Pattern

Start simple, add complexity based on demonstrated need. Small teams over invest in tools and under invest in processes. Large organisations need infrastructure but should resist premature complexity.

Conclusion

The AI adjacent tool landscape creates a peculiar pressure. Every week brings new platforms promising transformation. Marketing budgets ensure visibility. Early adopter stories generate excitement. The fear of falling behind becomes tangible.

Yet the pattern holds: markdown files still manage context effectively. Git still tracks decisions reliably. CLI tools still integrate systems efficiently. Your current IDE still supports productive work. For most teams, simple approaches remain sufficient longer than tool vendors would have you believe.

This doesn’t mean specialised tools lack value. Production observability prevents cost disasters. Workflow orchestration maintains complex automations. Knowledge management scales information discovery. The distinction lies not in whether tools matter, but in determining which problems justify which solutions.

The decision framework provides that distinction. Five questions transform tool evaluation from marketing-driven anxiety into evidence-based assessment. Most tools fail one or more criteria. Some pass all five. The framework saves you from both mistakes: adopting tools you don’t need and missing tools you do.

MCP illustrates this perfectly. A year after launch, with major industry backing and thousands of servers, it remains more valuable to platform builders than individual developers. CLI tools still work better for most use cases. This isn’t a failure of the protocol. It’s a reminder that sophisticated solutions require sophisticated problems.

Your competitive advantage comes from systematic thinking, not tool collection. The ability to evaluate whether simple approaches suffice, when specialised platforms justify their costs, and how to measure actual value over promised potential matters more than any specific tool choice.

The AI tooling landscape will consolidate. Standards will emerge. Your processes will outlast specific platforms. Build on foundations likely to persist. Invest in thinking that distinguishes between genuine value and marketing hype. Create infrastructure that adapts as tools come and go.

Before your next tool purchase, work through the framework. Try the simple approach first. Measure actual value. Remember that systematic thinking about problems and solutions provides lasting competitive advantage whilst tool choices remain contextual and temporary.

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About the Author

Tim Huegdon is the founder of Wyrd Technology, a consultancy that helps engineering teams achieve operational excellence through systematic AI adoption. With over 25 years of experience in software engineering and technical leadership, Tim specialises in developing practical frameworks for AI collaboration that enhance rather than replace proven development practices. His work on context management, documentation architecture, and human-AI collaboration patterns helps organisations build sustainable AI workflows whilst maintaining the quality standards that enable effective team collaboration.