How Sequential Thinking Changes Everything

Your AI is a brilliant intern with no short-term memory. It gives you an answer, forgets what it just said, and can't check its own work.

What if it could pause, think again, and fix its mistakes?

That's sequential thinking (step-by-step reasoning with the ability to look back)—and it changes everything.

What Sequential Thinking Actually Looks Like

Traditional AI: Robot bonks block with hammer (BONK!), then proudly presents result - Input, Output, Done, TA-DA!

Traditional AI: Input → Output → Done. No second thoughts. No revision.

Sequential Thinking: Robot pauses mid-answer, reconsiders with 'Wait... what about SQLite?', then explores multiple paths

Sequential thinking AI: "Wait... Actually no... Let me try..." — the AI pauses, thinks again, and tries other ideas before finishing.

The difference? The AI remembers its own steps and can fix them.

This isn't sci-fi.

Anthropic—the AI safety lab behind Claude—saw this gap. They built the Sequential Thinking MCP Server^[0]. MCP (Model Context Protocol) is a universal standard that lets AI plug into external tools—like USB-C for AI. This particular tool gives your intern a workspace to pause, think again, and fix mistakes.

TachiBot-MCP adds teamwork mode—bringing in different AI helpers, each good at something different. Your intern can now call in experts from 7 providers.

But how does this actually work under the hood? Three core mechanisms make it possible.

The Three Pillars

1. Saved Thought History

Developer realizing 'It... remembers what it tried?' while a French Bulldog with glasses holds a 'New Methodology?' clipboard

Every thought gets saved.

Say Thought #3 spots a problem: "We assumed the user has new software. But what if they're running an older version?"

The system writes down three things: this fixes Thought #1, which helper caught it, and when.

For the curious, here's what a single thought looks like in TachiBot:

// TachiBot nextThought tool
{
  thought: "We assumed new software. What if they have an older version?",
  thoughtNumber: 3,
  totalThoughts: 10,
  nextThoughtNeeded: true,
  model: "moonshotai/kimi-k2",  // Calling in a reasoning specialist
  isRevision: true,         // This thought fixes an earlier one
  revisesThought: 1         // It fixes Thought #1
}

See isRevision: true? That's the magic. The AI isn't just moving forward—it's looking back and fixing its own mistakes. And model: "moonshotai/kimi-k2"? That's TachiBot calling in Moonshot's reasoning specialist for the heavy lifting.

Why it matters: The system remembers what it tried. What worked. What failed. When Thought 5 builds on Thought 2, that link is real—not made up.

Tracking thoughts is powerful. But what happens when the AI needs to change course entirely?

2. Branching and Revision

Sequential Thinking: Breaking It Down - Robot tangled in wires says 'I'm stuck!', then breaks problem into steps with flowchart while French Bulldog approves

Here's where it gets interesting. Instead of straight-line thinking, the AI can:

Branch: "The standard approach uses a fast database. But what if we tried a simpler one? Let me explore both paths."

Revise: "Looking at Thought 3 again, I assumed the data comes in one format. The docs say it's actually a different format. Updating my plan..."

This is how humans solve hard problems. Not in a straight line. We explore. We backtrack.

Research on Tree of Thoughts^[1] confirms it: this approach dramatically improves performance on tasks requiring lookahead. The AI isn't just predicting the next word. It's searching for solutions.

One AI thinking step-by-step is good. But what if it could call for backup?

3. Multi-Model Orchestration

This is the core idea from Part 1: shared intelligence. When one specialist gets stuck, others step in. Every insight gets shared across the team.

Different specialists. Different strengths. Your intern assembles the right team:

The Builder → Claude Sonnet. Fast prototyper. Drafts code and ideas quickly.
The Critic → Grok. Skeptical reviewer. Spots flaws others miss.
The Architect → Claude Opus. Deep thinker. Handles the hardest problems.
The Researcher → Perplexity. Fact-checker with live sources.
The Coder → Qwen. Speaks 40+ programming languages.
The Librarian → Gemini. Reads entire codebases at once.

One intern. A whole expert team on speed dial.

Comic strip: Yellow robot admits 'I can't do all of this alone', specialists teleport in, French Bulldog assigns tasks - 'You all report to ME'

These three pillars need an engine to run on. Enter the dual-architecture approach.

The Architecture: How Your Intern Coordinates

TachiBot-MCP combines two proven approaches:

The Freelancer Model (MoE) — Your intern calls the right specialist for each task. Need code? Call The Coder. Need facts? Call The Researcher. No wasted effort.

The Assembly Line (MoA) — Specialists work in sequence. The Builder drafts. The Critic reviews. The Architect refines. Each layer improves the last. Research shows this beats solo work by 7+ percentage points^[2].

The result? Your intern only uses resources where needed—and catches blind spots no single expert would see.

Theory is nice. Let's see it solve a real problem.

A Reasoning Chain in Action

Let's see how this works for a real task: "Design a rate limiter for a high-traffic API."

Brainstorm → The Builder drafts 4 different traffic-control methods
Critique → The Critic reviews them: "Option A fails under sudden traffic spikes. Option B is overcomplicated."
Deep Dive → The Architect works through implementation... then pauses: "Wait—what happens when traffic hits multiple servers?" Revises approach.
Validate → The Researcher checks the documentation. Confirms the approach works. Flags an edge case.
Synthesize → The Librarian combines all insights into a final recommendation.

Five specialists. Five perspectives. One coherent solution—better than any single model could produce.

The magic is in Step 3. The Architect caught its own assumption mid-thought: "Wait—what happens when traffic spreads across multiple servers?"

Traditional AI would have shipped a solution that worked perfectly in testing—then collapsed in production. The sequential approach caught the flaw before it reached your users.

The Progress Guidance System

The orchestrator doesn't just run thoughts in sequence—it adapts guidance based on where you are.

Early on, it asks: "What assumptions are we making? What information is missing?"

In the middle, it pushes back: "What would a skeptic say? Have we explored alternatives?"

Near the end, it validates: "Are there edge cases we missed? Does this actually solve the original problem?"

Finally, it synthesizes: "What's the actionable recommendation?"

This mirrors how expert problem-solvers naturally think. But now it's explicit. Encoded into the system.

Dynamic guidance handles unique problems. But what about common ones?

Pre-Built Reasoning Chains

You don't have to build this from scratch. TachiBot-MCP includes ready-made templates:

Collaborative Reasoning — The Librarian researches → The Critic challenges → The Researcher validates → The Architect decides. Best for complex problems.
Architecture Debate — The Builder proposes → The Critic pokes holes → The Librarian judges. Best for design decisions.
Security Review — The Critic scans for vulnerabilities → The Architect maps risks → The Builder patches them. Best for security audits.

Pick a template. Plug in your problem. Let the team work.

Templates give you structure. But the real intelligence lies in what happens next.

The Self-Consistency Breakthrough

Here's a technique that supercharges everything above: repeat and compare^[3].

Don't trust your intern's first answer. Have them solve the problem three different ways. Take the answer that keeps showing up.

If three out of four approaches reach the same conclusion? That's probably right.

Research shows this alone dramatically improves accuracy. Combined with the specialist team, your intern runs multiple experts and multiple trials. Confident-but-wrong answers don't survive.

When to Use This

Sequential thinking shines for:

Complex debugging where the first fix often breaks something else
Architecture decisions with multiple valid approaches
Research synthesis requiring fact-checking across sources
Code review where different perspectives catch different bugs
Strategic planning with competing priorities

For simple tasks ("add two numbers"), it's overkill. Use it when the problem is genuinely hard. Or when getting it wrong is expensive.

Comic strip: Robot sweats over DEPLOY button imagining disasters (explosion, server fire, sad Frenchie, aliens, black hole). ZOOP - pulls hand back. French Bulldog checks watch: 'Waiting for API response... 45s'

Ready to upgrade your intern?

Try It Yourself

TachiBot handles the thinking. DevLog-MCP handles the remembering.

Together, they give your intern a team of specialists and a memory that persists across sessions.

Get started →

References

[0] Anthropic: Sequential Thinking MCP Server (GitHub, December 2024)

[1] Shunyu Yao et al.: Tree of Thoughts: Deliberate Problem Solving with Large Language Models (arXiv, May 2023)

[2] Junlin Wang et al.: Mixture-of-Agents Enhances Large Language Model Capabilities (arXiv, June 2024)

[3] Xuezhi Wang et al.: Self-Consistency Improves Chain of Thought Reasoning in Language Models (arXiv, March 2022)

This is Part 2 of a series. Part 1 explains the problem.