They’ll be the best orchestrators.

This is the single biggest shift in Anthropic’s 2026 Agentic Coding Trends Report — and most startup engineering teams aren’t ready for it.

For years, engineering value was measured by how much you could build.

Lines of code shipped. Features delivered. PRs merged.

That metric is dying.

Here’s what’s actually happening right now:

Agents can complete 20 autonomous actions before needing human input. Six months ago, that number was 10. The trajectory is clear.

The engineer who keeps their head down writing every line of code isn’t your most valuable engineer anymore.

The engineer who knows how to direct agents — precisely, efficiently, safely — is.

For enterprise teams with 200 engineers, this is a restructuring problem.

For startup engineering teams, this is a superpower.

A 5-person startup team that orchestrates agents well doesn’t just punch above its weight.

It punches like a 25-person team.

So what does the “orchestrator mindset” actually look like in practice?

It’s three things:

1. Define roles with precision, not prompts Stop asking agents to “build a feature.” Start defining them like team members — this agent owns the schema, this one writes the API, this one runs security checks. Ambiguity in a prompt wastes hours. Precision in a role definition multiplies output.

2. Set guardrails before you set them loose What can the agent decide autonomously? What must come back to a human? The teams winning with agentic engineering aren’t the ones giving agents the most freedom. They’re the ones who’ve drawn the clearest lines. Autonomy within constraints — that’s the startup engineering way.

3. Your job is validation, not generation You’re no longer the person writing the code. You’re the person who catches what the agent gets wrong, spots the architectural drift, and makes the calls that require real-world judgment. That’s a higher-leverage role — if you embrace it.

Here’s what I’ve seen firsthand:

In the last 8 months leading a 14-person AI engineering team at a startup, the engineers who adapted fastest weren’t the ones with the deepest coding skills.

They were the ones who could think in systems. Who could break a problem into agent-sized pieces. Who could review output with a sharp eye and move on.

The mindset shift happened before the tools did.

Startup engineers have always done more with less.

We’ve always had to think in systems because we couldn’t afford to throw bodies at problems.

We’ve always had to ship with constraints because runway doesn’t wait.

Agentic coding didn’t change our game.

It just made our game worth 10x more.

The engineers who will define the next decade of startup engineering aren’t learning to code faster.

They’re learning to orchestrate smarter.

Vibe Coding promised the world. Worked great for prototypes. Failed miserably in production. Startups burned runway. Engineers burned trust.

The skepticism is earned.

But something changed in November 2025.

Agents weren’t a new concept. The capability was new.

When Claude Opus 4.5 dropped, reasoning depth, context handling and multi-step execution crossed a real threshold. The technology finally caught up to the vision.

2025 was the breakthrough. 2026 is the adoption year.

The 3 stages of how we got here:

🔵 Stage 1 — Autocomplete Faster typing. Same thinking. Productivity gain, not transformation.

🟡 Stage 2 — Vibe Coding Great for greenfield. Collapsed on brownfield. This is where startups got burned.

🟢 Stage 3 — Agentic Coding Works on real production codebases. Agents plan, execute and validate — end to end. Your engineers define outcomes. Agents own execution.

Why this matters for your startup:

You can’t out-hire a funded competitor. But you can out-leverage them.

Early-stage to Series A startups can’t compete on headcount. Agentic Product Engineering is the leverage that levels the field.

So I created a pattern to solve this once and for all.

The Problem:

• Startups scaling from 1 to 3+ MCP servers face:

• Security logic duplicated across every server

• No central control over agent access

• Scattered observability

• Security vulnerabilities multiplying

• Especially painful for 5-15 engineer teams shipping fast without compromising security.

The Solution: MCP Gateway Pattern

A centralized gateway between AI agents and MCP servers. Handle cross-cutting concerns once.

What it handles:

1. Security: Tool shadowing prevention, injection defense

2. Auth/AuthZ: JWT validation, RBAC/ABAC, dynamic tool filtering

3. Routing: Server discovery, dynamic config, health checks

4. Tool Registry: Centralized allowlist, collision detection, validation

5. Policy Engine: Input sanitization, output DLP, human-in-the-loop

6. Observability: Single dashboard, distributed tracing, metrics, audit logs

7. Traffic Management: Rate limiting, circuit breakers, load balancing, caching

Why This Works:

• Build MCP servers without auth boilerplate

• Update security policies centrally

• Debug in minutes with centralized logs

• Control costs through central rate limiting

Key Insights:

• Single source of truth saves weeks of duplicate work

• Adds 10-50ms latency but eliminates hours of debugging

• Start simple (auth + logging) → evolve to sophisticated policies

• Use existing solutions over building from scratch

• Circuit breakers from day one

When to Use:

3+ MCP servers • Multiple agents • Production systems • Teams without dedicated security

When NOT to Use:

Single agent setups • Prototypes • Small teams (< 5 engineers)

How to Adapt:

• Don’t build everything day 1 → Start with auth + logging

• Don’t build from scratch → Use managed or open source

• Don’t skip circuit breakers → Prevent cascade failures

• Don’t skip observability → Log everything

Implementation Options:

Managed Solutions (Buy): AWS Agentcore Gateway • Azure AI Gateway • TrueFoundry • Composio • Lunar.dev

Open Source (Build): Docker MCP Gateway • IBM mcp-context-forge • Lasso Security MCP Gateway • Obot

Choose managed for speed. Choose open source for control and cost optimization.

Traditional testing gave us false confidence.

• Unit tests passed

• Integration tests passed

• Code review approved

• 𝗕𝘂𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗳𝗮𝗶𝗹𝗲𝗱

𝗪𝗵𝘆 𝗔𝗜 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁:

AI agents are non-deterministic. Standard testing doesn’t work.

You need to validate what traditional testing ignores:

→ Semantic correctness (Does it understand intent?)

→ Reasoning correctness (Does it think logically?)

𝗙𝗼𝗿 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀, 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 = 𝘀𝘂𝗿𝘃𝗶𝘃𝗮𝗹.

No second chances. Every bug erodes trust you can’t rebuild.

𝗪𝗵𝗮𝘁 𝗜 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱:

Issues in one evaluation stage cascaded downstream → untraceable production failures.

The fix? Compartmentalize. Test tools separately from reasoning, reasoning separately from outputs, outputs separately from metrics.

Last week, Google’s “𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗚𝘂𝗶𝗱𝗲: 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀” validated exactly what I learned the expensive way. The guide has the multi-evaluation framework for evaluating AI agents.

𝗧𝗵𝗲 𝟰-𝗟𝗮𝘆𝗲𝗿 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸:

𝗟𝗮𝘆𝗲𝗿 𝟭: Component Testing Deterministic unit tests for tools and APIs → Catches 90% of basic failures → Takes 2 days to set up. Don’t skip it.

𝗟𝗮𝘆𝗲𝗿 𝟮: Trajectory Evaluation ⚡ 𝗠𝗢𝗦𝗧 𝗖𝗥𝗜𝗧𝗜𝗖𝗔𝗟 Validate reasoning path, not just outputs → Does it reason correctly? → Right tools with right parameters? → Does it learn from outputs?

𝗟𝗮𝘆𝗲𝗿 𝟯: Outcome Evaluation What users actually see: → Factual accuracy → Helpfulness → Completeness

𝗟𝗮𝘆𝗲𝗿 𝟰: System Monitoring Track real-world performance: → Tool failure rates → Cost per conversation → User feedback scores

𝗪𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱:

Issues cascade. Compartmentalize testing—saved weeks of debugging.

Trajectory validation cut debugging time 95%.

Cost monitoring isn’t optional for startups.

𝗪𝗵𝗮𝘁 𝗜’𝗱 𝗱𝗼 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆:

Don’t: Ship based on “it looks good in demos” Do: Build evaluation into CI/CD from day one

Don’t: Test only final outputs Do: Validate every reasoning step

Don’t: Skip component testing Do: Start with Layer 1. Always.

P.S: Google’s “Startup Technical Guide: AI Agents” link in comments.

Domain experts wait days for prompt iterations. Engineers can’t keep up. Deployment takes weeks.

The question isn’t “do we have the right AI model?”

It’s “do we have the right architecture?”

What I Discovered:

I studied how BlackRock reduced AI development from 8 months to days. What struck me wasn’t their tools or team size—it was their architectural philosophy.

Traditional approach:

- Modularize by business logic

- Build app per use case

- Engineering owns everything

BlackRock’s approach:

- Modularize by iteration speed

- Build platform once

- Domain experts own experimentation

The key insight: Optimize for who needs to iterate fastest, not how features are organized.

Their Platform Architecture:

Sandbox → Domain experts experiment

- Edit prompts directly (no engineering ticket)

- Test LLM strategies in minutes

- Run evals on document sets

App Factory → Auto-generates production apps

- Takes sandbox configurations

- Deploys via API automatically

Platform → Engineers build foundation once

- Data platform

- Governance and risk management

- Standardized API layer

The Transformation:

Development: 8 months → Days

Prompt iterations: Days → Minutes

Strategy testing: Weeks → Hours

Deployment: Weeks → Automated

Who iterates: Engineers → Domain experts

Startup Engineering Principles:

This isn’t specific to BlackRock. These principles work for any team—10 people or 10,000—operating in startup mode.

As a founder for 5 years, I learned this the hard way: In startup mode, everything is a hypothesis. Your architecture and process should enable rapid validation.

Why this matters for AI teams: Most things are still hypotheses. Shipping faster isn’t optional—it’s survival.

Where to Start:

Identify your slowest validation loop. Give domain experts direct access. Measure before/after. If you see 5-10x improvement, expand.

P.S: Thank you to infant vasanth and Vaibhavpage from BlackRock for sharing this at AI Engineer World’s fair 2025

A single user request triggers 200+ LLM calls. Which one failed? Where did context break? Our MTTR became as high as 7 hours. As a startup, we cannot afford to have our reputation at stake.

The Startup Dilemma:

Everyone suggested: LangSmith, LangFuse, Helicone.

• Evaluating tools = 2–3 weeks

• Every new tool = learning curve + cost + vendor management

• We don’t even know what we need yet

First-Principles Questions:

What exactly do we need to debug? Input sent, response received, post-processing results, and relationships between 200+ calls.

The insight: Not a logging problem. It’s a tracing problem. Each LLM call is stateless. Our application maintains context. Same applies to observability.

The Solution:

We already had Grafana Loki, Tempo, and OpenTelemetry.
The breakthrough: Connect all LLM calls under one parent span.

Architecture :

1. Application Layer
   OpenTelemetry emits logs and traces.

2. Logs (Grafana Loki)
   Captures each LLM interaction:

   1. trace_id & span_id

   2. Message sent & response received

3. Traces (Grafana Tempo)

   Parent span connects all related calls:

   1. “Analyze PDF” → Factor 1 (Profit) → Factor 10

   2. Each factor → LLM Call 1… Call 20

   3. Shows serial vs parallel execution, timing.

4. Observability UI (Grafana)
   Query by span_id → Complete journey with all logs.

The Results:

• MTTR: 7 hours → 30 minutes

• Timeline: 5 days (2 design + 2 dev + 1 infra)

• Cost: $0 in new tools

• Complexity: Zero new vendors

Key Design Decisions:

Why Traces Over Logs?
Logs show events. Traces show relationships. For 200+ interconnected calls, relationships matter.

Why Existing Stack Over New Tools?
Existing stack solved 80%. New tools solve 100% but cost 5x in time, money, and complexity.

Why Parent-Child Spans?
Mirrors how our application maintains state. One request → multiple factors → multiple LLM calls.

Startup Engineering Lessons:

Don’t: Add new tools before understanding the problem.
Do: Define what you need first (saved 3 weeks).

Don’t: Evaluate 5 tools when you don’t know what you need.
Do: Use existing stack, learn what’s missing, choose deliberately.

Don’t: Wait for the “perfect” solution.
Do: Ship an 80% solution in 5 days, iterate based on production issues.

We’ll eventually need specialized AI observability tools. But not today. Tomorrow, we’ll know exactly which tool solves which problem.

That’s Startup Engineering: choose the simple over the perfect solution. Solve for 80%. Iterate to 100%.

After experimenting with different patterns and reading countless papers, I’ve settled on a layered architecture that balances simplicity with production-readiness.

Why I built this:

Most agent architectures I found online were either:

• Too academic (great for papers, terrible for building)

• Too simplistic (demos that break at scale)

• Too enterprise grade (over engineered for Startups)

So I designed something practical for Startups

The Four-Layer Approach:

1. Interface Layer - Multiple Entry Points

• Human-Agent: Standard chat interface

• Agent-Agent: For multi-agent collaboration

2. Agent Layer - The Core System

This is where it gets interesting. Three distinct sub-layers:

2.1 Orchestration (Coordination)

• I/O Layer for all data flow

• Workflow Manager for process control

• Multi-agent coordination for scaling

2.2 Workflow (Execution)

• Plan: Break down complex tasks

• Reflection Learning: Continuous improvement

• Tool Use ↔ MCP Client: Execute with external tools. MCP (Model Context Protocol) standardizes how agents connect to external systems. No more writing custom integrations for every API.

2.3 Intelligence (Cognition)

• Model: LLM with prompt engineering and fine-tuning

• Memory: Context (short-term) + Knowledge (long-term)

This separation lets the agent remember conversations while building institutional knowledge over time.

3. Integration Layer - External World

MCP Servers provide standardized access to:

• Datastores

• Filesystems

• APIs

• Custom tools

4. Governance Layer - The Difference Between Demo and Production

This is what everyone skips. Don’t.

• Evaluation: Measure quality

• Monitoring: Track performance and costs (yes, costs matter!)

• Guardrails: Prevent harmful outputs

• Security: Protect sensitive data

For engineering teams: This layer saves debugging hours and prevents costly production issues. Build it from day one.

Key Insights from Building This:

1. Reflection Learning is your competitive advantage

2. MCP changes everything

3. Memory architecture matters more than model choice

4. Governance isn’t optional

5. Multi-agent coordination unlocks scale

What I’d Do Differently:

Don’t: Build custom tool integrations

Do: Use MCP from the start (saved weeks)

Don’t: Fine-tune immediately

Do: Perfect your prompts first (70% cost reduction before considering fine-tuning)

Don’t: Skip the Reflection component

Do: Let your system learn from every run (this is your moat)

Don’t: Treat Memory as an afterthought

Do: Design it early - it’s harder to retrofit

Don’t: Build without monitoring

Do: Track costs, latency, and success rates from day one

What struck me most was how dramatically engineering priorities must shift as startups evolve from Inception → Stabilization → Growth → Maturity. Getting this wrong at any stage can be the difference between thriving and merely surviving.

Here’s what great startup teams do differently at each stage:

Inception: Build with speed and clarity

The Reality: You’re racing to build your first release before running out of runway. But speed without focus kills startups just as surely as perfectionism does.

What Matters Most:

• Assemble the right 2-4 person team – The study found that team capability matters more than team size. You need domain knowledge AND engineering expertise. One without the other leads to either building the wrong thing well, or the right thing poorly. If you’re missing skills, consider targeted outsourcing rather than hiring too early.

• Invented requirements need rapid validation – At this stage, 90% of your product ideas are invented, not discovered. Your job isn’t to build everything you imagine—it’s to invalidate your assumptions as quickly as possible with real users. Use mockups and prototypes, not production code, for validation.

• Scope your MVP ruthlessly – The most common mistake? Building features that won’t validate your core hypothesis. Ask: “Does this feature test our value or growth hypothesis?” If not, kill it. One startup in the study reflected: “Any requirement essential for validating the hypotheses is priority. Anything to support the business when the user base is larger than 100 users is not a priority.”

• Choose boring technology – 67% of startups in the study used established, stable frameworks rather than cutting-edge tech. Why? Mature technologies have communities, documentation, and fewer surprises. Save your innovation budget for your product, not your infrastructure.

• Keep testing manual but systematic – Automated testing can wait. But manual testing shouldn’t mean random clicking. Use informal exploratory testing with clear scenarios. It’s faster to set up and more valuable when requirements change daily.

The Hidden Trap: The biggest danger at inception isn’t moving too slowly—it’s building with too many people or without clear accountability. The study found a significant association between closed companies and lack of team accountability. Stay small, stay focused, stay aligned.

Stabilization: Strengthen the core

The Reality: You’ve launched. You have early customers. Now comes the hard part—evolving the product without collapsing under technical debt while your team is still tiny.

What Matters Most:

• Establish your customer feedback loop – This is THE defining practice of this stage. The data is stark: 91% of active startups actively collected customer input, versus only 43% of closed startups. Set up regular customer interviews, usage analytics, and support ticket reviews. Make customer input directly drive your product roadmap.

• Fight feature creep relentlessly – With customer feedback flowing in, you’ll be tempted to build everything everyone asks for. Don’t. 30% of startups at this stage fell into the feature creep trap, draining resources and adding complexity. Use the Pareto principle: only build features that 80% of customers will use. Everything else is a distraction.

• Document requirements—even informally – You can’t keep everything in your head anymore. Write down feature ideas, their source, and rationale. Even simple Trello cards beat relying on memory. This isn’t bureaucracy—it’s building organizational knowledge as your team grows.

• Define “good enough” quality explicitly – You can’t afford perfect, but you can’t afford broken either. Startups that succeeded were deliberate about quality thresholds. One stated: “We look for the simplest solution that can support a couple hundred users. How can it be improved to support more?” Set clear minimum bars for performance, reliability, and UX.

• Start addressing technical debt strategically – Architecture decisions that worked for 10 users will break at 1,000. The study found that teams with adequate skills were more likely to recognize and address technical debt early. Review your architecture quarterly. Budget 15-20% of engineering time for refactoring the most painful areas.

The Hidden Trap: Scaling your product before scaling your team processes. Teams that jumped from 3 to 10 people without establishing clear responsibilities, communication norms, and decision-making processes hit serious coordination challenges. Invest in team structure now, before it becomes crisis management.

Growth: Scale without chaos

The Reality: You’re no longer just building—you’re operating, supporting, selling, and iterating simultaneously. Engineering decisions now directly impact revenue, and business goals directly impact your roadmap.

What Matters Most:

• Balance customer value with business goals – This is where value definitions shift. It’s no longer just about what users want—it’s also about monetization, market expansion, and differentiation. One startup noted: “We prioritize using this question: how much money or new customers will we gain if we implement this feature?” Engineering must have a seat at the business strategy table.

• Make metrics non-negotiable – 80% of growth-stage startups tracked both external metrics (revenue, customer satisfaction, usage) and internal metrics (team velocity, deployment frequency, bug rates). Without metrics, you’re flying blind. Implement basic analytics, error tracking, and performance monitoring. Review metrics weekly with the entire team.

• Hire for specialist skills strategically – 80% of growth-stage startups reflected on needing specialist skills (specific domain expertise, advanced technical skills, security experts). But hiring too many specialists too fast creates coordination overhead. Hire specialists for force-multiplier roles—someone who can uplevel your entire team, not just do specialized tasks.

• Invest in test automation NOW – Manual regression testing becomes unsustainable at this stage. The study found growth-stage teams spending substantial effort manually testing the entire product before releases. Automate your most painful regression tests first. Start with smoke tests, then expand. Even 30% test automation dramatically accelerates releases.

• Address technical debt aggressively – Technical debt peaks at the growth stage and becomes a major velocity killer. Architecture debt is particularly insidious—one startup reflected: “Initially, the product was started by an inexperienced developer and many design decisions were incorrect. Fixing them early would have been easy, however with the product and user base growing, changing core things became increasingly difficult.” Allocate 20-30% of engineering time to paying down the most critical debt.

The Hidden Trap: Treating growth as purely a sales/marketing problem. The study found that engineering bottlenecks—slow releases, poor quality, inability to support multiple customer segments—often capped growth more than market factors. Engineering infrastructure must scale ahead of demand, not react to it.

Maturity: Operate like an orchestra

The Reality: You’re no longer a plucky startup—you’re a real software company with distributed teams, complex deployment pipelines, and enterprise customers. Your engineering practices need to reflect that maturity.

What Matters Most:

• Formalize project management without killing agility – 87% of mature startups had established project management processes with tools, sprint planning, and resource planning. But these weren’t heavyweight waterfall processes—they were lightweight frameworks that brought visibility without bureaucracy. Implement structured sprints, clear roadmaps, and capacity planning. Use tools like Jira or Linear, but keep ceremonies minimal.

• Establish a dedicated QA function – Manual testing by engineers doesn’t scale to large, complex products. Mature startups introduced QA specialists or QA teams responsible for test strategy, automation, and quality gatekeeping. This frees engineers to focus on building while maintaining quality.

• Build for portability and multi-tenancy – As you expand to new markets or customer segments, your architecture must support multiple configurations, languages, compliance requirements, and deployment models. Portability wasn’t a concern at earlier stages but becomes critical now. Design for configuration, not customization.

• Optimize for team coordination, not individual productivity – With 20+ person teams, often distributed, communication and coordination become the bottleneck. Mature startups invested in documentation, clear interfaces between components, and structured handoffs. Architecture decisions should minimize dependencies between teams.

• Measure and optimize team performance – Mature startups tracked internal efficiency metrics: deployment frequency, lead time for changes, time to restore service, change failure rate (the DORA metrics). These metrics reveal organizational bottlenecks that individual productivity measures miss.

The Hidden Trap: Assuming maturity means slowing down. The best mature startups maintained startup speed through better processes, not despite them. They used automation, clear ownership, and disciplined prioritization to move faster than their younger competitors while maintaining higher quality.

The Meta-Lesson: Evolution is the Only Constant

The most striking finding from the study wasn’t about any single stage—it was about the dramatic shifts between stages. What makes you successful at inception (small team, invented requirements, manual everything) will break you at growth. What’s premature at stabilization (formal QA, extensive metrics) becomes essential at maturity.

The startups that succeeded were those that recognized these shifts and deliberately evolved their practices. The startups that failed often got stuck applying inception-stage practices to growth-stage problems, or prematurely optimizing at inception for maturity-stage needs.

Three practices that matter at every stage:

1. Customer connection – From inception to maturity, maintaining direct customer feedback loops correlated with success. Never let processes get in the way of listening.

2. Deliberate quality decisions – Whether defining your MVP scope or enterprise SLAs, explicitly deciding what quality means prevented both under-engineering and over-engineering.

3. Team capability over process – At every stage, teams with strong domain knowledge and engineering skills could compensate for immature processes. Skills matter more than frameworks.

The question isn’t “What are the best software engineering practices?” The question is “What are the right practices for our stage, and how do we know when to evolve?”

The startups that answer that question thoughtfully are the ones that survive to maturity—and beyond.

The paper analyzed 84 software startups across Europe and South America, using case survey methodology to identify patterns in engineering goals, challenges, and practices. The full citation: Klotins et al., “A progression model of software engineering goals, challenges, and practices in start-ups,” Journal of LaTeX Class Files, 2014.

What stage is your startup at? What engineering practices are working—and which ones are holding you back? Reply and let me know—I read every response.

The rules of engineering have fundamentally changed. We’re no longer optimizing for 2x growth—we’re building for 10x impact.

What makes AI-era engineering different?

𝗕𝘂𝗶𝗹𝗱 𝗳𝗼𝗿 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄’𝘀 𝗺𝗼𝗱𝗲𝗹𝘀, 𝗻𝗼𝘁 𝘁𝗼𝗱𝗮𝘆’𝘀
Foundation models are getting 10x better and cheaper every 12-24 months. Smart engineers are architecting systems that leverage future capabilities, not just current ones.

𝗥𝗲𝘁𝗵𝗶𝗻𝗸 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗳𝗶𝗿𝘀𝘁 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀
When Cursor reimagined coding assistance or Bolt compressed website building from hours to minutes, they didn’t iterate—they rebuilt. The best AI products aren’t improvements; they’re replacements.

𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗻𝗼𝘃𝗲𝗹 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀
RAG, voice, reasoning—these aren’t just features to add. They’re the foundation of entirely new user experiences. The question isn’t “how do we add AI?” It’s “how does AI enable what was previously impossible?”

𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗳𝗼𝗿 𝗿𝗮𝗱𝗶𝗰𝗮𝗹 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆
AI-native companies achieve $500K+ revenue per engineer (vs $60-100K traditional). This isn’t about doing more with less—it’s about building fundamentally different architectures where AI agents handle what armies of humans once did.

𝗧𝗵𝗲 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝘀𝗵𝗶𝗳𝘁:
From “how do we make this 20% better?” to “how do we make this unquestionably superior?”
From protecting existing workflows to completely reimagining them
From building for the $1T software market to the $10T AI-augmented labor market

We’re at the starting line of a wave projected to create $20-25 trillion in market value. The companies winning aren’t just using AI—they’re letting AI redefine what’s possible.

Looking at the spectrum, successful implementations begin in the bottom left (supervised, simple goals) and methodically work toward the top right (full autonomy, complex goals). The path isn't linear or quick, but this measured iterative approach actually delivers value faster than attempting to jump straight to high agency from day one.

Many projects fail because they aim too high, too fast. Instead, successful AI implementation follows a progression:

1) Start with supervised systems handling simple goals - low agency
2) Gradually increase complexity while maintaining supervision
3) Incrementally grant more autonomy as performance proves reliable
4) Finally reach high-agency solutions through deliberate iteration

Happy Engineering !

These patterns aren't just theoretical frameworks—they represent practical approaches that developers and organizations are implementing today to enhance AI capabilities and deliver more sophisticated solutions. Let's explore each of these patterns in depth.

1. Reflection: The Self-Improving Loop

Perhaps one of the most powerful capabilities of modern LLMs is their ability to critique their own outputs. By implementing reflection, we prompt an AI system to:

1. Generate an initial solution or response

2. Evaluate that solution against specific criteria

3. Identify areas for improvement

4. Generate a refined solution based on this feedback

This creates a powerful iterative loop that mimics the human editing process. For example, when tasked with writing code, an LLM can first generate a solution, then "step back" to identify potential bugs, inefficiencies, or edge cases before producing an improved version.

The beauty of reflection lies in its simplicity and effectiveness. Without requiring additional tools or resources, it significantly enhances output quality through what is essentially a self-dialogue process.

2. Tool Use: Extending AI's Reach

While LLMs contain vast amounts of knowledge, they still have inherent limitations. The tool use pattern addresses these limitations by enabling AI systems to interact with external resources through API calls. This pattern allows LLMs to:

• Retrieve real-time information via web searches

• Execute code to perform calculations or data analysis

• Access private or proprietary data sources

• Interact with productivity applications (email, calendar, etc.)

• Generate visual content or process images

By integrating with these external tools, AI systems transcend their role as purely conversational interfaces and become capable of taking concrete actions in the digital world. A well-implemented tool use pattern transforms an LLM from a knowledge repository into a proactive assistant that can bridge the gap between understanding and doing.

3. Planning (or Reasoning): The Strategic Approach

Complex problems rarely have straightforward solutions. The planning pattern addresses this reality by prompting LLMs to break down complex tasks into logical sequences of steps before attempting to solve them.

When implementing this pattern, we ask the AI to:

1. Analyze the overall objective

2. Identify key components and dependencies

3. Establish a sequential plan of action

4. Consider potential obstacles and alternative approaches

5. Execute the plan methodically

This approach mirrors how human experts tackle difficult problems—by thinking strategically before acting. For example, when asked to optimize a database query, an LLM using the planning pattern might first outline the steps: understand the current query structure, identify bottlenecks, consider indexing strategies, rewrite the query, and finally test performance improvements.

The planning pattern significantly enhances an AI's ability to handle multi-step problems that require structured thinking and helps avoid the pitfalls of rushing directly to solutions without adequate preparation.

4. Multi-Agent Collaboration: The Power of Specialization

Perhaps the most sophisticated pattern discussed by Andrew Ng is multi-agent collaboration. This approach involves creating multiple instances of LLMs (or the same LLM at different times) that play distinct roles in solving a problem.

In a multi-agent system, we might see:

• A researcher agent that gathers information

• A critic agent that evaluates proposals

• A creative agent that generates novel solutions

• A summarizer agent that consolidates findings

• A coordinator agent that manages the overall workflow

This pattern leverages the power of specialization and diversity of perspective. By having different "experts" focus on specific aspects of a problem, the overall quality of the solution improves. This mimics successful human team structures where individuals with different strengths collaborate toward a common goal.

Multi-agent systems are particularly effective for complex tasks that benefit from multiple perspectives, such as content creation, strategic planning, or comprehensive research projects.

Combining Patterns for Maximum Impact

While each of these patterns is powerful on its own, the most sophisticated AI systems often combine multiple patterns to create robust workflows. For instance, a system might use planning to break down a complex task, tool use to gather necessary information, multi-agent collaboration to generate and critique solutions, and reflection to refine the final output.

As these design patterns mature and become more widely implemented, we're seeing the emergence of AI systems that can tackle increasingly complex, open-ended problems with greater autonomy and effectiveness.

Looking Ahead

The patterns outlined by Andrew Ng represent the current state of the art in agentic AI design, but this field is evolving rapidly. As LLMs continue to advance and new techniques emerge, we can expect these patterns to evolve and new ones to develop.

For developers and organizations looking to implement AI agents, understanding these fundamental patterns provides a valuable framework for designing effective systems. Rather than viewing AI as a monolithic solution, these patterns encourage us to think about the specific capabilities we want to enable and the most effective ways to structure AI workflows.

What's particularly exciting is that these patterns are accessible and implementable today. Whether you're building enterprise-grade AI solutions or experimenting with personal projects, these design patterns offer practical approaches to enhancing AI capabilities and delivering more value.

The results? Significantly improved output quality and the ability to handle increasingly complex challenges. There will be remarkable performance improvements in everything from coding to legal document processing.

Here's what's truly fascinating: while we often hear about fully autonomous AI agents as a distant future goal, agentic workflows are giving us a practical stepping stone today. They're teaching us how AI can break down complex tasks, self-reflect, and iterate - fundamental building blocks for more autonomous systems. It's like learning to walk before we run.

The beauty lies in the practicality: organizations can start implementing these workflows now, gaining immediate benefits while laying the groundwork for more advanced AI capabilities.

This shift in terminology from "agents" to "agentic" represents three fundamental changes in our understanding:

First, it acknowledges that AI capabilities exist on a 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐬𝐩𝐞𝐜𝐭𝐫𝐮𝐦 rather than in discrete categories. Just as we recognize varying degrees of intelligence or autonomy, we can appreciate different levels of agentic behavior in AI systems.

Second, it promotes inclusivity in AI development. By moving away from binary classification, we create space for systems that exhibit different degrees of agent-like behavior. This encourages innovation at all levels, 𝐟𝐫𝐨𝐦 𝐬𝐢𝐦𝐩𝐥𝐞 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 𝐭𝐨 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐬𝐲𝐬𝐭𝐞𝐦𝐬.

Third, it better reflects the reality of AI system development. As Andrew Ng points out, "There's a grey zone between what clearly is not an agent (prompting a model once) and what clearly is (say, an autonomous agent that, given high-level instructions, plans, uses tools, and carries out multiple, iterative steps of processing)."

This perspective shift has practical implications for AI development, allowing teams to incrementally enhance their systems' agentic capabilities without being constrained by rigid definitions. It encourages a more nuanced and practical approach to building AI systems that can effectively serve their intended purposes."

The economics are shifting dramatically. When high-quality AI becomes this affordable, it's not just an incremental change—it's a complete transformation of what's possible. The 𝐫𝐞𝐚𝐥 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐨𝐫 is no longer the model itself, but 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐛𝐮𝐢𝐥𝐝 𝐰𝐢𝐭𝐡 𝐢𝐭.

We're entering an era where the "𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐥𝐚𝐲𝐞𝐫" 𝐢𝐬 𝐛𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐜𝐨𝐦𝐦𝐨𝐝𝐢𝐭𝐢𝐳𝐞𝐝, pushing value creation upstream to applications. With models like DeepSeek delivering enterprise-grade performance at consumer-grade prices, the barriers to building impactful AI solutions have never been lower.

This is why I believe we're at an inflection point. The combination of high intelligence and low cost means thousands of previously unviable use cases suddenly make business sense. But here's the catch—we need more builders. 𝐌𝐨𝐫𝐞 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬. More problem solvers who can bridge the gap between powerful models and real-world problems.

If you're considering a career pivot or wondering where to focus in tech, the AI application layer is calling. This isn't just another tech cycle—it's a once-in-a-generation opportunity to build something meaningful.

AI Engineering is an emerging discipline that focuses on building applications and solutions using pre-existing, powerful AI models. Unlike traditional software engineering or machine learning engineering, AI Engineering leverages foundation models – large, pre-trained AI models capable of a wide range of tasks – to create practical, real-world applications.

Key aspects of AI Engineering include:

• Adapting foundation models for specific tasks

• Integrating AI capabilities into applications

• Optimizing AI performance for production environments

• Evaluating and fine-tuning AI models for specific use cases

AI Engineering has gained prominence due to the availability of powerful models like GPT-4o (OpenAI), Claude 3.5 Sonnet (Anthropic), Gemini 1.5 Pro (Google), Llama 3.2(Meta) and others, which can be accessed through APIs and integrated into various applications.

“AI Engineering focuses on building applications using pre-existing, powerful AI models like GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3.2.

AI Engineering, ML Engineering, and Software Engineering

AI Engineering combines elements from both Software Engineering and ML Engineering, but with a unique focus on adapting and applying pre-trained AI models. While an AI Engineer may not need the deep mathematical knowledge required for ML model development, they do need to understand how to effectively use and adapt these models within software applications.

This interdisciplinary nature of AI Engineering makes it an exciting and accessible field for those with backgrounds in either software development or data science. It allows for the rapid development of AI-powered applications without the need for extensive machine learning expertise or the resources required to train large models from scratch.

To better understand the role of an AI Engineer, it's helpful to compare it with related fields: ML Engineering and Software Engineering. The following Venn diagram illustrates the similarities and differences among these disciplines:

ML Engineering vs AI Engineering

While ML Engineering and AI Engineering share some similarities, there are key differences:

Software Engineering vs AI Engineering

While AI Engineering builds upon traditional software engineering principles, it introduces new concepts and challenges:

In conclusion, AI Engineering represents a paradigm shift in how we build software applications. It combines elements of software engineering, machine learning, and emerging AI technologies to create powerful, adaptive applications. As we progress through this guide, you'll gain insights into the AI Engineering lifecycle, essential knowledge and skills, and practical steps to start your journey in this exciting field.

Agentforce is a low-code/no-code platform for building autonomous agents. For example, customers can create customer service agents using natural language, and these agents can operate 24/7.

Why is it a Big Step?

In 1999, Salesforce revolutionized traditional software by introducing SaaS. Now, it's taking a groundbreaking step into AI with its Agentforce platform. AI agents are seen as the future of software.

Why Does It Matter to the AI Engineering Community?

We’re starting to witness the “There’s an AI agent for that” moment. The evolution of software applications will look like this:

On-prem Software → SaaS → AI Applications → AI Agents.

An AI agent can perform much more abstract tasks than an AI application. For example, you can delegate the task of planning a vacation. The agent can “think,” break down subtasks like booking hotels and flights, and then execute those tasks.

Is It Just Another Hype?

I believe there may be some hype, but the AI transformation in the application space is real. If companies like Salesforce, which have enterprise customers, find value in AI agents, we are moving closer to making it a reality.

Key Takeaway:

If you’re an AI engineer, you should know how to build AI agents. If you’re a startup, you should start focusing on building AI agents instead of traditional AI applications.

Types of AI Applications:

• End-to-End AI Applications: Built entirely in-house, these applications offer tight integration but demand significant resources—time, money, and expertise.

• Foundation Model led AI Applications: With the rise of foundation models like GPT-4o, companies like OpenAI and Google have democratized AI by offering these models through APIs, allowing instant access and faster time-to-market.

Challenges with Foundation Model led AI applications:

• Applications have less control over the model

• Model is typically designed for generic use cases.

• Scaling AI application can lead to rapidly increasing costs.

The Solution: AI Model Adaptation

To tailor these foundation models to your specific needs, you need to tweak or adapt the AI Model to suit your use case. This process called “AI Model adaptation” ensures that the AI aligns with your unique use case, providing the flexibility needed to overcome the limitations of generic models.

Popular AI Model Adaptation Methods:

• Prompt Engineering

• Retrieval Augmented Generation

• Parameter Efficient Fine-Tuning

• Instruction Fine-Tuning

𝗖𝗼𝘀𝘁 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: RAG is significantly more cost-efficient than LC LLMs, making it a viable option when computational resources are limited.

𝗦𝗘𝗟𝗙-𝗥𝗢𝗨𝗧𝗘 𝗠𝗲𝘁𝗵𝗼𝗱: The authors propose SELF-ROUTE, a method that dynamically routes queries to either RAG or LC based on model self-reflection, achieving comparable performance to LC while reducing costs significantly.

𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: LC LLMs like Gemini-1.5 and GPT-4 consistently outperform RAG across various datasets, with more recent models showing a larger performance gap.

𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Common failure reasons for RAG include multi-step reasoning, general queries, long and complex queries, and implicit queries, providing a direction for future improvements.

𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: The hybrid approach of SELF-ROUTE optimizes both cost and performance, making an advanced AI applications more accessible and efficient.

#AI #AIEngineering

Exploring the security aspect has often been overlooked in the frenzy of integrating Foundation AI Models into business operations and client-facing applications since late 2022. This rapid adoption has been nothing short of remarkable. However, it has outpaced the establishment of comprehensive security protocols, leaving many AI applications vulnerable to high-risk issues.

Then I discovered the 𝗢𝗪𝗔𝗦𝗣 𝗧𝗼𝗽 𝟭𝟬 𝗳𝗼𝗿 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 project, which provides a crucial resource for understanding and mitigating these vulnerabilities. The top 10 most critical vulnerabilities include:

𝗪𝗵𝗼 𝗦𝗵𝗼𝘂𝗹𝗱 𝗖𝗮𝗿𝗲?

All stakeholders involved in this AI Engineering space to design, develop and manage AI applications - Developers, Data Scientists, Security Experts, Business Leaders.

𝗪𝗵𝘆 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗖𝗮𝗿𝗲?

𝗣𝗿𝗼𝘁𝗲𝗰𝘁 𝗦𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: Avoid leaks of confidential and proprietary data.

𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝗧𝗿𝘂𝘀𝘁: Ensure clients and users trust your AI applications by demonstrating a strong commitment to security.

𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲: Meet regulatory requirements

𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗶𝘁𝘆: Prevent disruptions caused by security incidents, such as denial of service attacks or data poisoning.

By following the OWASP Top 10 for AI applications, we can significantly enhance the security posture of our AI projects. This list is not just a guideline but a crucial tool for ensuring that the powerful capabilities of AI models are harnessed safely and responsibly.