ENROLLINGAI Platform Engineering · Cohort 1 · starts 25 July · 50% off till 2 July
n2 · BRAIN AS A SERVICE · THE TEKTON LABS MANIFESTO
One brain. Any AI platform. Dozens of surfaces.

CREATE THE INFRA BRAIN.SOLVE ENTERPRISE'S HARDEST PROBLEM.BE A TEKTON — MASTER BUILDER.

A universal knowledge graph exposed over MCP with 27 tools. Plug in Claude Code · Cursor · Codex · Windsurf or your own agent — every surface below rides the same brain.

AGENTS · IN PRODUCTION
CONTEXTLYTeam Copilot
Grammarly for distributed teams.
Drafts every reply with thread history + past ADRs + who you're talking to.
hey team can u check the checkout thing
n2 · thread × 42 · ADR-007 · @rafa
Hey @rafa — the checkout p99 issue from ADR-007? Similar pattern. Datadog trace linked.
live · rides n2
SHERLOCKAI SRE Agent
RCA in seconds. Before the war room opens.
Walks traces × deploys × config across 1,000+ services → root cause + PR.
live · rides n2
PERSONAL JARVISContinuous Memory
Developer brain across every AI.
Every session in Claude/Cursor/Codex inherits your projects, decisions, past threads.
live · rides n2
ORG DASHBOARDReal-time State
Real-time state of the entire org.
Services, teams, incidents, revenue — read straight from the graph. No CI, no dashboards.
live · rides n2
AGENTS · SHIPPING NEXT
PAIR CODERArchitectural Awareness
Not autocomplete. Architectural awareness.
Suggestions load service graph, owners, past incidents. They FIT your org.
func processPayment(...) error {
  // n2 → owner: @payments-team
  // n2 → past incident: pg-lock
  txn := db.Begin(ctx)
  ...
Claude · org context loaded
live · rides n2
CODE REVIEWERGuardrail Agent
Blocks OOMKills before merge.
Simulates every PR against the brain. Flags HPA thrash, migration locks, blast radius.
live · rides n2
COST + SCALEOptimization Agent
Right-size before finance asks.
Walks utilization + HPA history + cloud bills. Opens PRs with proposed savings.
live · rides n2
SLACK COPILOTIn-channel Agent
The graph walks in Slack.
@sherlock in #war-room. Reply cites the offending PR, dashboard, and proposed fix.
live · rides n2
ONE BRAIN · EIGHT SURFACES

Every agent below rides the same graph.

n2's 8 core use-cases straight from docs/adr/003-usecases.md. Learn to build them in the course.

AI SRE · Sherlock agent
RCA in seconds
Walks traces × deploys × config across 1,000+ services → root cause + PR while you finish reading Slack.
● pulling from brain
datadogcheckout p99 spike
See how it works
CODE REVIEWER agent
Blocks OOMKills before merge
Reads every PR against the graph — HPA thrash, migration locks, cross-service breakage. Blocks bad merges.
● pulling from brain
githubPR #4218 · +318 / −57
See how it works
SLACK COPILOT agent
In-channel graph walker
@sherlock in #war-room. Every reply cites the offending PR, dashboard, and proposed fix — grounded in the graph.
● pulling from brain
slacksara: why is checkout slow?
See how it works
COST + SCALE agent
Right-size before finance asks
Walks utilization + HPA history + cloud bills. Proposes limit trims, instance flips, scale-down schedules. Opens PR.
● pulling from brain
awscheckout +32% MoM
See how it works
PERSONAL JARVIS agent
Developer brain, every AI
Claude / Cursor / Codex inherit your projects, tasks, code sessions, meeting notes, decisions. Continuous memory.
● pulling from brain
githubPR #4218 · your last change
See how it works
PAIR CODER agent
Architectural awareness
Not autocomplete. Knows your codebase AND org context — service graph, owners, past incidents — every suggestion fits.
● pulling from brain
graphwalking svc graph · 3 hops
See how it works
ORG DASHBOARD agent
Real-time state of the org
Services, teams, incidents, decisions, dependencies — read straight from the graph. Ask a question. No CI. No builds.
● pulling from brain
k8s1,000+ services · scanned
See how it works
CONTEXTLY agent
Grammarly for distributed teams
Every Slack reply gets thread history, past ADRs, and who you're talking to pre-loaded. Miscommunication doesn't ship.
● pulling from brain
slackthread · 42 msgs
See how it works
UNLIMITED · BUNDLED
Free AI tokens
with every course seat
  • ModelAnthropic models
  • Value$200 / month · 5 months
  • Total$1,000 / seat credit
  • Runs onPuku CLI (bundled)
Enroll now — first 30 seats get 2× tokens
PUKU CLI · v1.8
Puku CLI
Sherlock RCA · same model as Claude
LIVE
puku-cli — sherlock-rca
TEKTON LABS · LEARN TO SOLVE PRODUCTION PROBLEMS AT ENTERPRISE SCALE
  • THE PROBLEM
    1,000+ services scream at once
    Modern platforms are a wall of dashboards, alerts, and pages. Engineers spend hours stitching context by hand — while customers wait.
  • THE APPROACH
    One brain that walks the graph
    You build a correlated knowledge graph that ingests every source — traces, deploys, configs — and answers 'why is this broken' in seconds.
  • WHAT YOU SHIP
    An AI SRE that talks in Slack
    You ship Sherlock — an agent that lives in your team's channel. It runs RCAs, opens PRs, and drafts post-mortems while you sleep.
  • AND MORE
    Cross-service code review agent
    A reviewer that reads the PR, walks the whole service graph, and blocks changes that would OOMKill or break a downstream contract — before merge.
  • OUR MISSION
    Train engineers to build all of it
    20 weeks. 60 live classes. Free AI tokens. Puku CLI shipped on day one. You leave with the code, the skills, and the confidence.
Tekton Labs · Learn to solve production problems at enterprise scale

1,000+ services. RCA in seconds.
Learn to build the SRE agent that gets you there.

Modern engineers don't just write services — they operate them across hundreds of clouds, thousands of pods, and a wall of dashboards. This course teaches you to build the platform that cuts through it: a correlated infra brain, an AI SRE agent in Slack, a cross-service code-review agent for your PRs, and Puku CLI with a free bundle of Anthropic-grade tokens. Ship it in 20 weeks.

60
live classes
7
systems shipped
20
weeks
1
canvas · humans + agents
Why we're launching AI Platform Engineering

Enterprise RCA is broken.
We built the fix. Now we teach it.

Tekton Labs' first course trains the next generation of engineers to solve the RCA + platform problems modern infra teams actually face. This is the story of why the course exists — told through the exact system you'll ship.

01
THE PROBLEM

On-call has no shared brain.

An alert fires. Every dashboard has a slice of the answer. Nobody has the whole picture. So the war room opens — and it stays open.

DATADOG dashboard
DATADOG
traces · logs · metrics
GRAFANA dashboard
GRAFANA
16 dashboards open
TENCENT dashboard
TENCENT
cvm · tke · clb
AWS dashboard
AWS
cloudwatch · x-ray
K8S dashboard
K8S
40 pods · 3 clusters
SLACK
#war-room · 214 unreads
WAR ROOM · 03:14 AM · P1

eng·1 any correlation on the p99?

eng·2 still pulling traces…

eng·3 which dashboard has the deploy?

eng·2 ES heap. finally. 4h 12m.

4h 12m time to RCA
7 engineers pulled in
0 context reused
02
THE ANSWER — SHERLOCK

Central context. The org's Infra DNA.

In this course, you build Sherlock — a command-line SRE that keeps the entire org's operational context in one place. Type one question, get the RCA. In minutes, not hours. Sherlock is the first system you ship — and it becomes the surface everything else in the course plugs into.

sherlock — 03:14 AM
$ sherlock rca vat-calculator
→ pulling last 100 traces from Datadog
→ walking dependency graph (3 hops)
INC-2061 · SYMPTOMP1
vat-calculator · 94.8%
247,574 / 261,270 · mango-prod-tencent
CLOUDFLAREnot the cause
edge 5xx only because backend 5xx
AWSnot the cause
ELB pass-through · nodes nominal
K8Shot
opensearch-processor · heavy
ARGOCDnot the cause
no recent deploys
TENCENT OSSMOKING GUN
ES heap 15GB > limit 14.3GB
★ ROOT CAUSE · opensearch-processorBATCH INGEST

PricePromoOpenSearchService.processVatData bulk price/promo ingest saturated the ES heap, tripping the circuit breaker — blocking all reads from vat-calculator.

FIXscale ES → 16 CPU / 64 GB · circuit breaker in vat-calculator · PR #4218 opened
03
POWERED BY THE INFRA BRAIN

Every source. Every signal. One graph.

Sherlock is a face. The Infra Brain is the muscle behind it — a correlated knowledge graph that ingests every source (Datadog, K8s, ArgoCD, cloud consoles, PR history) and walks them at query time. It's what turns 'traces plus deploys plus config' into a single walkable answer. Here's a Black Friday spike the brain solved in seconds.

INCOMING SIGNAL · BLACK FRIDAY · 08:42 UTC
/checkout p99 spiked 400ms → 1.4s · 42k rpm · $12k / min at risk
Ask Sherlock /sherlock-rca checkout-service — the Infra Brain walks the graph. Below is what the brain saw.
checkout-service
walking graph…
STRIPE
upstream · payments
webhook lag 8ms · nominal
REDIS
session + cache
hit rate 92% → 61% · evictions up 4x
POSTGRES
orders · primary
conn pool 78/100 · pgbouncer at limit
K8S
checkout hpa · 8→22
HPA scaled ✓ · node pressure ok
GITHUB
deploys · pr history
PR #4218 · +reco_engine · 12h ago
DATADOG
apm · traces · sli
/checkout p99: 400ms → 1.4s
★ CAUSAL CHAIN · 4 HOPS · 94% CONFIDENCE · walked 8sUNCACHED PATH

PR #4218 shipped a new product-recommendation module on/checkout. It calls reco_engine.rank() on every render, which bypasses the Redis session cache and hits orders_dbwith an uncached SELECT per request. Under Black Friday load (42k rpm), Postgres pgbouncer hits its pool ceiling — every subsequent checkout waits ~1s for a connection. K8s scaled the checkout tier (8 → 22 pods), but the DB was the real bottleneck — scaling made it worse.

FIX · shipping in 4mCache reco_engine in Redis (5-min TTL) · lift pgbouncerpool 100→200 · flag-gate the feature behind p99 < 800ms · rollback plan armed.
$12k / min revenue at risk
8s brain walk
4m 20s mitigation deployed
0 pages issued
04
AI SRE IN SLACK

Sherlock, meet the humans.

Wrap the brain in a Slack app so the whole org talks to it. Ask a question in #war-room. The agent parses the intent, the brain walks the graph across every source, and the answer lands back in the channel — with the RCA, the fix, and the PR already open.

#
war-room
32 members · SRE + platform
● live
SM
S M Shahriar Islam10 May · 5:41 AM

@Sherlock rca for marketplace-checkout-service in mango-prod-tencent

reply
🤖
SherlockAPP10 May · 5:41 AM
RCA — marketplace-checkout-service

Summary: 98% of errors on POST /payment/webhook — malformed UUIDs hitting PostgreSQL, causing DB syntax errors across all retries.

Root cause: webhook handler passes a truncated UUID (e68360dce6364e…) that pg/lib/client.jsrejects with invalid input syntax for type uuid. 4 ERR spans per trace — retries amplify.

Mitigation: uuid validation on webhook · pg schema check · rollout to mango-prod-tencent in 4m. PR opened · on-call notified.

👀 3✅ 7🙌 2
SHERLOCK
AI SRE Agent
✓ parsed intent — rca
→ dispatching to Infra Brain…
brain query
INFRA BRAIN
DATADOG
POSTGRES
K8S
GITHUB
REDIS
STRIPE
→ 4 hops · 6 sources · walking correlations
posted to #war-room
05
CODE REVIEW AGENT

A reviewer that knows the whole graph.

Because the Infra Brain already tracks every service, endpoint, dependency and Helm chart — a review agent built on top of it can do what no linter can. It reads the PR, knows the upstream callers, spots memory-limit bumps that will OOMKill on rollout, and calls out blast-radius on the review, not the file. This is Phase 6 — the review your senior engineer wishes they'd get.

feat: cache full product catalog in checkout-service #4218
● Openk-okoye wants to merge 4 commits into main·+318 / −57 across 7 files
✓ build (14s)✓ unit (2m 04s)✓ lint✗ sherlock/blast-radius✗ sherlock/infra-safety
S
Sherlock Code Review Agent
walked 5 services · ran 18 checks · 2m ago

Reviewed 7 files across 5 dependent services. Non-trivial blast radius — flagging 5 issues before merge. Bumping catalogCache from 50 items → full catalog reaches into inventory-sync, pricing-svc and promo-service.

⚠ K8s memory / OOMKillblocking

Full catalog push takes checkout-service resident memory to ~680 Mi. Current limits.memory = 512 Mi every pod OOMKills ~40 s after rollout. Required: bump to ≥ 1 Gi in charts/checkout/values.yaml.

charts/checkout/values.yaml
-  memory: 512Mi
+  memory: 1Gi
⚠ HPA thrash on cold-startblocking

Cold-start with the full catalog spikes CPU to 1400m for ~90 s. Your HorizontalPodAutoscaler targets 80% CPU with minReplicas: 3 — brain sim shows a scale-up storm to 27 pods then scale-down within 2 min. Add a warmup gate or raise stabilizationWindowSeconds to 300.

⚠ Cross-service breakage · inventory-syncneeds review

/cache/invalidate is called 200 req/s by inventory-sync (owner: @inventory-team). The new 30 s TTL collides with a batch-tick every 25 s, producing ~5 s of stale reads per cycle. Downstream promo-service depends on this contract for coupon eligibility.

⚠ DB migration · exclusive lockblocking

202607_add_catalog_index.sql runs CREATE INDEX without CONCURRENTLY on products (48 M rows). Brain observed a ~9 min AccessExclusiveLock on the last similar migration — which will block orders-api writes and page @dbre.

db/migrations/202607_add_catalog_index.sql
-CREATE INDEX ix_products_sku ON products(sku);
+CREATE INDEX CONCURRENTLY ix_products_sku ON products(sku);
⚠ Secret in code + SLO burnblocking

src/cache/redis.ts:47 hardcodes a Redis password — should be process.env.REDIS_AUTH. Also: brain projects a 2.4× 30-day SLO burn on checkout.p99<300ms if the HPA thrash isn't fixed.

src/cache/redis.ts
- password: "r3d1s-prod-9x8Kk!"
+ password: process.env.REDIS_AUTH,
Risk Score
CRITICAL · 9.4 / 10
Blast radius: 5 services · 3 teams · checkout inventory-sync promo-svc pricing-svc orders-apiauto-requested: @platform-team · @inventory-team · @dbre
Sherlock will block this merge. Fix the 4 blockers and re-run /sherlock review — I'll re-simulate against the brain and clear it.
DETAILS · ONE PER AGENT

Eight surfaces, one brain — here's how each one plays.

Every agent below is what BaaS makes possible. All ride the same graph — powered by the correlation engine, the skill harness, and the federated knowledge providers you build in the course.

  • CORRELATION ENGINE12-step pipeline · store → parse → resolve → temporal / topological / semantic sweeps → notify agents
  • SKILL HARNESSSkills as graph nodes · schema-free learned extraction · versionable, editable, learnable
  • KNOWLEDGE PROVIDERSFederated master-slave · KubeKnowledge · CodeKnowledge · ObsKnowledge · progressive enrichment
  • MCP LAYER27 tools · exposed to Claude Code · Cursor · Codex · your custom agent · stdio + HTTP
AI SRE · SHERLOCK

RCA in seconds — before the war room opens.

Sherlock joins an incident, listens, walks the correlated graph across every source, and posts the root cause with a PR fix before three people have finished asking questions in Slack.

  • Sources it walkstraces · deploys · pod events · runbooks · PRs
  • Time-to-RCA~4 min from alert to PR (median · production)
  • How n2 powers itCorrelation engine + entity graph over 1,000+ services
sherlock — rca
$ sherlock rca checkout-service
◆ pulling last 100 traces from Datadog…
◆ correlating with Postgres logs…
◆ walking service graph · 3 hops
✓ Analysis complete · 4m 12s
Root Cause:
✗ Postgres OOMKill · unique_violation on customer_email_key
✗ Deploy #4211 · 42s before spike
✓ PR #4218 opened · @on-call notified · #war-room updated
CODE REVIEWER

Blocks OOMKills before merge.

Every PR gets simulated against the brain. Blast radius, HPA thrash, migration locks, cross-service breakage — flagged with concrete fixes. The review your senior engineer wishes they'd get.

  • Signals it checksK8s limits · HPA history · dep graph · SLO burn
  • Where it blocksInline PR comment + failing check
  • How n2 powers itCross-service graph queries at MCP layer
feat: cache full product catalog #4218
● Open+318 / −57 · 7 files · 5 dependent services
✓ build✓ lint✗ n2/blast-radius✗ n2/oomkill-sim✗ n2/migration-lock
⚠ K8s memory / OOMKillblocking

Full catalog push takes checkout memory to ~680 Mi. Current limits.memory = 512 Mi — pods will OOMKill 40s after rollout. Bump to ≥ 1 Gi.

n2 will block this merge. Fix and re-run.
SLACK COPILOT

The graph walks in Slack.

@sherlock in #war-room isn't just answering — it's citing the offending PR, the dashboard, the past incident, the owning team. Every reply is grounded in the graph.

  • Trigger@mention · thread reply · slash command
  • Answer includesPR link · dashboard · fix · past incidents
  • How n2 powers itQuery→graph-walk pipeline, results grounded in evidence
#war-room
S
sara
@sherlock why is checkout p99 spiking?
sherlock APP
Walking the brain across traces deploys k8s events
sherlock APP
RCA: HPA thrash 42s after deploy #4211. Similar incident 2 months ago (ADR-004).
Fix: raise stabilizationWindowSeconds to 300.
PR #4218 opened · @platform-team auto-requested
COST + SCALE

Right-size before finance asks.

Walks utilization + HPA history + cloud spend. Proposes limit trims, instance-type flips, scale-down schedules. Opens the PR itself. You approve. Bill drops next month.

  • Data sourcesAWS billing · CloudWatch · HPA · Grafana
  • OutputPR against Helm values + rollout plan
  • How n2 powers itTime-series joins across billing and infra graphs
tekton — cost
$ tekton cost --window 30d --top
◆ AWS +32% MoM · checkout-svc (primary offender)
◆ HPA over-scaled 27/8 avg pods · 3× over target
◆ instance type: 3× t3.large @ 12% CPU avg
◆ recommend: 3× t3.large → 3× t3.medium
◆ recommend: scale-down schedule · off-hours 3 pods
✓ Save $2.4k/mo · PR #4230 opened
Impact preview:
≈ 32% cost drop · zero SLO regression predicted
PERSONAL JARVIS

Your developer brain — every AI, every session.

Continuous memory across Claude, Cursor, Codex. Your projects, tasks, code sessions, decisions, meeting notes — all pre-loaded, all handed to whichever AI you're using right now.

  • What it remembersPRs · decisions · Slack threads · docs · past sessions
  • Where it livesn2 personal graph · exposed via MCP
  • How n2 powers itPer-user knowledge graph with cross-AI session sync
loading your continuous memory
githubPR #4218 · your last change · 2d ago
meetingdecision · Q3 design review · you argued for MCP
slack#war-room · the incident you led last week
docsrunbook · payments-api · your author line
cursorsession · yesterday · you were rewriting auth
brain42 items handed to Claude · continuous
PAIR CODER

Architectural awareness, not autocomplete.

Every code suggestion loads your entire codebase + org context first — service graph, owners, past incidents on this code. Suggestions fit YOUR org, not a generic StackOverflow answer.

  • Load-time contextcallers · owners · past incidents · deploy history
  • Editors supportedClaude Code · Cursor · Codex · custom
  • How n2 powers itLive MCP handoff — graph walked at completion time
// package payments — checkout flow
func processPayment(ctx context.Context, o *Order) error {
  // n2 → owner: @payments-team
  // n2 → past incident (2mo ago): pg-lock in retry loop
  // n2 → adjacent service: fraud-check calls this
  txn, err := db.Begin(ctx)
  if err != nil { return err }
  defer txn.Rollback()
  ...
}
Claude · using org context handed by n2 · 3 signals
ORG DASHBOARD

Real-time state of the entire org.

One question in Slack. Snapshot of every service, on-call rotation, active incident, revenue impact, pending PR — read straight from the graph. No CI. No manual dashboard building.

  • Query surfaceSlack · web · MCP tool call
  • Latency~200ms for a full org snapshot
  • How n2 powers itMaterialized entity pages + templated composer
?state of the org · right now
services1,247 scanned · 4 degraded · 0 down
on-callsara · 2h left · sherlock covers 3–7am
incidents2 open · #checkout · #ao-connector
revenueimpact: $12k/hr while degraded
deploys8 today · 1 rolled back · owner: @platform
prs23 open · 6 blocked by n2 · 4 awaiting review
✓ posted to #leadership · no builds, no CI
CONTEXTLY

Grammarly for distributed teams.

You draft a Slack reply. n2 quietly loads the thread history, past ADRs, and who you're talking to. Claude rewrites the draft with the missing context inline — miscommunication doesn't ship.

  • Context surfacedthread history · ADRs · people · past decisions
  • Where it worksSlack drafts · GitHub review comments · doc drafts
  • How n2 powers itPer-conversation graph subgraph handed to Claude
hey team can u check the checkout thing
n2 · thread 42 msgs · ADR-007 · rafa joined 3d ago
Hey @rafa — the checkout-service p99 issue you flagged Tuesday? Similar pattern to ADR-007 (Postgres retry loop). Datadog trace here → linking. Want me to open a PR?
✓ context inline✓ named the person✓ linked past decision
n2 · BUILDING IN PUBLIC

We're open-sourcing Brain as a Service.

n2 is a universal knowledge graph — infra, code, docs, incidents, ownership — exposed over MCP. Claude Code, Cursor, Codex, or your own agent plugs in and walks a pre-correlated graph. Same brain we run against 1,000+ services in production. Same brain you learn to build in the course.

  • 27
    MCP tools
  • 8+
    agent use-cases
  • 4
    layers · collector → provider → brain → mcp
  • MIT
    license · yours to run
TEKTON LABS · MASTER BUILDERS FOR n2

We're building Tektons — join us.

A Tekton is a Master Builder for n2 · Brain as a Service. The AI Platform Engineering course is where you become one — 20 weeks, live, alongside the engineers open-sourcing n2. Ship the same brain we run in production. Let's create Brain as a Service together.

  • 20
    weeks · live cohort
  • 60
    sessions
  • 7
    systems you ship
  • 1
    brain · you build a subset in the open
The course funds the open source. The open source is what the course builds. Same brain, both sides.
BUNDLED WITH THE COURSE

Meet Puku CLI your terminal, upgraded.

The same terminal experience Anthropic gave the world — but wrapped in a Tekton-native CLI with 13 production skills, an operator-friendly UI, and a free bundle of AI tokens for every cohort seat. Every command you'll type in the course, you type here.

puku-cli · v1.8.38
Puku CLI welcome screen with skill autocomplete
DAY 0
All skills. All ready.
Autocomplete for every command in the syllabus.
  • AI

    Same experience as Claude, in your terminal

    Puku CLI wraps Anthropic-grade models in a native terminal UI. Chat, code, review PRs, run RCAs — without leaving your shell. Course students get a bundled key on day one.

  • TOKENS

    Free AI tokens included with the course

    Every cohort seat comes with a real Anthropic token budget — enough to build the seven course systems from scratch without paying for AI. Yours to keep after the course.

  • SKILLS

    13 pre-built skills for real ops work

    Sherlock skill pack ships with Puku CLI: /sherlock-rca, /sherlock-services, /sherlock-failing-services, /sherlock-traces, /sherlock-health, /idp-login, /idp-deployments, and more — every command in the course syllabus.

  • OWN

    Yours forever. No lock-in.

    Puku CLI is the exact tool you build in the course — the CLI is open. The token budget is yours. You leave with the source, the skills, and the credits.

SKILLS REGISTRY13 shipped · slash-command autocomplete
Puku CLI skills listing — sherlock and idp commands
/sherlock-rca/sherlock-services/sherlock-traces/idp-deployments/idp-platform-health+ 8 more
REAL RCA OUTPUTSlack-ready markdown · shipped in 3 minutes
Puku CLI RCA output — Postgres unique constraint violation
customer_email_key · 48/50 sampled errors · p99 ~10s · data-corruption-shaped
BUNDLED · COHORT 1
Puku CLI + AI tokens · included in AI Platform Engineering
No separate purchase. No subscription. Yours forever after enrollment.
The course, as a mind map

Course → Module → What you actually build.
Nothing hidden. Every branch is a real artifact.

TEKTON LABS · AI PLATFORM ENGINEERING
Cohort 1 · 20 weeks · 60 live sessions
  1. 1
    Multi-Cloud CLI
    tekton-cli v1 · Ship day 15
    • ├──Query Datadog + AWS CloudWatch + K8s from one binary
    • ├──Auth abstractions across 4 clouds
    • └──Local cache + config for team-wide reuse
  2. 2
    K8s Data Plane
    tekton-cli v2 · Ship day 32
    • ├──kubectl-grade exec, port-forward, tail across clusters
    • ├──One binary, four clouds, zero context switches
    • └──Multi-cluster namespace + context registry
  3. 3
    MCP + Enterprise RCA
    tekton-cli v3 · Ship day 52
    • ├──Build an MCP server exposing your infra to Claude / Cursor
    • ├──Register tools + resources with structured schemas
    • └──Your IDE becomes an on-call terminal
  4. 4
    Infra Brain
    Correlated Knowledge Graph
    • ├──Ingest traces, logs, metrics, deploys into a graph
    • ├──Walk cascades at query time — 3 hops, 94% confidence
    • └──The nervous system every skill talks to
  5. 5
    AI SRE Slack Bot
    Sherlock · Your team's on-call
    • ├──Slack skills: /sherlock-rca, -services, -traces, -failing
    • ├──3-minute RCA from cold pager to opened PR
    • └──Post-mortems written, on-call routed, humans looped in
  6. 6
    Cross-Service Reviewer
    Ship day 132
    • ├──Reviews PRs across 100+ microservices
    • ├──Contract-aware diffs — knows every downstream caller
    • └──Comments on the review, not the file
  7. 7
    Cross-Cutting Tracks
    Security · Cost · Reliability · Eval
    • ├──SOC-2 hygiene woven into every skill from lesson 1
    • ├──Cost budgets attached to every deploy
    • └──Reliability + evaluation — production from day one
What Tekton Labs is

One canvas.
Humans and agents, learning together.

Static blogs are done. On Tekton Labs, every post is a diagram — pan it, annotate it, ask the AI tutor about it, turn it into a lab in one click, and share the same canvas with your team. Read, learn, collaborate, ship. The eight layers below are the platform that makes it work.

01

Collaborative platform · Human + Agent

One canvas for everyone — write docs, run analytics, design diagrams, draft emails, ship code. Humans and agents work side by side on the same surface.

// canvas → agent → canvas — bidirectional
02

Connector Layer

Generic YAML-driven connectors pull data from every source — cloud, K8s, observability, code, docs, comms. Add a new source with 20 lines of YAML, no code.

connectors/datadog.yaml → auto-ingest
03

Infra Brain — Correlated Graph

Every signal is stitched into a queryable knowledge graph — services, traces, deploys, owners, code, incidents. One MCP call returns full context.

get_entity("checkout") → full context
04

Skill Hub

Reusable skills — RCA, deployment analysis, code review, cost audit. Compose skills into higher-level agents. Progressive loading keeps token cost low.

skills/rca-cascade.md → composed
05

Agent Communication · Trigger-based

Agents react to events — PagerDuty fires, PR opens, deploy fails, brain detects drift. Trigger → agent runs → posts back to Slack / GitHub / canvas.

on:incident → rca-agent → post-slack
06

Skill Enhancement

Agents improve themselves — auto-detect stale skills, propose skill updates via PR, backfill missing docs, learn from resolved incidents.

self-heal · propose PR · learn from resolves
07

Infra Provisioning

Draw the architecture on canvas → Tekton generates Terraform / Pulumi / K8s manifests → provisions and wires it up. Diagram-driven infra.

canvas.draw → terraform → deploy
08

AI SRE Slack Bot

The end-user surface — engineers ask questions in Slack, get full RCA in seconds. Approval-gated actions. Sandboxed execution. Cost-capped per team.

@Tekton rca checkout-service in prod
Who Tekton Labs is for

One canvas — four ways to use it.
Learn on your own · with a team · as a cohort.

Every blog on Tekton Labs is a canvas. Every canvas can be a lab. Every lab has an AI agent. And every canvas is one URL away from being shared with your team. Read → learn → collaborate → ship — never leave the tab.

  • READERS

    Learn systems the way engineers actually work

    Read a blog, ask the floating agent for context, turn the same page into a scratchpad and take notes on top of the diagram. Never leave the canvas.

    Free
    reading + agent Q&A
  • MOST POPULAR
    LEARNERS

    Turn any blog into an interactive lab

    One click on any post. The reader becomes a canvas you can extend, the agent becomes a teacher, and the exercise saves back to your account. Progress persists across visits.

    ৳499 / mo
    unlimited labs · unlimited agent
  • SOLUTION ARCHITECTS

    Diagram, review, and ship — with your team

    Share a canvas link. Teammates open it in the browser and draw with you in real time. Ask the agent to critique the architecture. Export to Terraform. This is the whiteboard your team was already trying to build inside Miro.

    ৳1,999 / seat / mo
    team canvas · SSO · export
  • COHORT STUDENTS

    Go end-to-end in 20 weeks

    The AI Platform Engineering course — 60 live sessions, 7 shipped systems, real production incidents, code-review from Makro's platform team. Cohort 1 starts 25 July 2026.

    ৳10,000
    one-time · 50% off till 2 July
COURSES· 1 flagship live · 2 self-paced coming
FLAGSHIP · COHORT 1 · STARTS 25 JULY 2026

AI Platform Engineering
Learn to build Tekton, end to end.

60 live classes across 20 weeks. You ship the connectors, the brain, the skill hub, the agent communication layer, the AI SRE Slack bot, and the code-review agent — the exact platform Tekton runs on, built with your own hands, on the canvas.

60
live classes
7
shipped projects
20
weeks
AI tokens

50% early-bird off ends 2 July · seats limited to keep the live sessions high-touch.

Who’s teaching this

I ship this stack every day.
Now I teach exactly how it works.

I lead the Platform Engineering + AI infrastructure work at CP Axtra — one of Thailand’s largest retailers, running 1,000+ microservices across AWS, Tencent Cloud, GCloud, and Alibaba Cloud. I am also CTO & Co-Founder of Tracewit AI, where we build the infra-brain + agent stack this course teaches. Every skill you build here is a piece of the actual platform we operate in production.

  • 1,000+
    microservices in production
  • 4
    clouds · one control plane
  • ~$15B
    annual revenue behind uptime
  • 3 min
    last RCA with Sherlock
FOUNDER · LIVE

Meet the founder behind Tracewit — live at the wheel.

The same stack this course teaches, built in real time. Hover to preview, click to watch the full recording from 14:14.

FOUNDER · LIVE
Founder behind Tracewit — building the AI SRE, live
Shahriar Islam · Sherlock in Slack · same stack this course teaches
1h 12m
Watch on YouTube ↗
ONGOING COURSES

Currently enrolling · Poridhi · Forward Deployed Engineering

PREVIOUS COURSES

Interactive Cares · Backend Engineering with Golang · DevOps Engineering

Interactive Cares
Backend Engineering with Golang
Ship a real ride-sharing / food-delivery backend — Kong · GraphQL · Redis · Tile38 · Temporal · BigQuery.
REELSPromo reels from both courses — autoplay muted. Click any to open the full video on Facebook.
FROM LINKEDIN

Recent posts · @devshahriar

Syllabus · 20 weeks · Cohort 1 enrolling

8 phases. You ship the whole Brain.

No slides for slides' sake. Every phase ends with a capstone you can demo — a real piece of BaaS plugged into the running graph. By week 20 you have shipped a working multi-cloud AI SRE platform on top of the correlation engine, skill harness, and MCP layer you built yourself.

Phase 0Weeks 1–2

Foundations

ships: Environment + primitives
  • Platform Engineering · SRE · CI/CD · IaC
  • Networking · observability fundamentals
  • Why AI changes platform engineering
  • Setup: Docker Compose · Postgres · Temporal
Phase 1Weeks 3–5

Puku CLI + First Data Sources

ships: CLI shipping RCAs from Datadog + AWS
  • Puku CLI as the right primitive for AI agents
  • Datadog + AWS connectors · auth + rate limits
  • First Skill: /sherlock-rca — end-to-end RCA
  • Skills as graph nodes (foundation of Phase 3)
Phase 2Weeks 6–8

K8s Data Plane · Own Your Raw Store

ships: K8s agent + Kafka + OpenObserve
  • K8s agent + multi-cluster gateway (KubeKnowledge)
  • Kafka message bus · sink to OpenObserve / Quickwit
  • Redaction scanner (17 rules) · compliance by default
  • Evidence tiers: observed > declared > claimed > inferred
Phase 3Weeks 9–11

Skill Harness — Learned Extraction

ships: Skill platform · schema-free parsers
  • Skills as graph nodes · versionable, editable, learnable
  • Skill induction: LLM promoted to deterministic parser
  • Skill health · outcomes tracking · self-repair loop
  • Zero silent drops — every field extracted has provenance
Phase 4Weeks 12–15

Correlation Engine + Knowledge Providers

ships: The Brain · federated sub-graphs
  • 12-step pipeline: Store → Parse → Resolve → Sweep → Notify
  • Temporal · Topological · Semantic sweeps
  • Federated providers: KubeKnowledge · CodeKnowledge · ObsKnowledge
  • Entity resolution · stable IDs · pgvector for semantic edges
Phase 5Weeks 16–17

MCP Layer — Any Agent Plugs In

ships: MCP server · 27 tools · Claude / Cursor / Codex
  • MCP server (stdio + HTTP) · 27 brain query tools
  • Session sync, skill sync, note-writing, verification
  • Templated entity pages · $0 per query (no LLM)
  • Auto-approve strategy + credential proxy
Phase 6Weeks 18–19

AI SRE Slack Bot — Sherlock in Production

ships: Slack app that runs RCAs live
  • Slack Bolt + Claude Agent SDK · thread-aware
  • Webhook intake (PagerDuty, GitHub, Datadog alerts)
  • Sandboxed runtime · approval-gated destructive actions
  • RCA · fix draft · PR opened · postmortem — end to end
Phase 7Week 20

Code Review Agent — Blast Radius

ships: PR blocker that walks the whole graph
  • Simulates PR against the brain · pre-merge
  • Infra impact: OOMKill · HPA thrash · migration lock
  • Cross-service breakage · owner routing · risk score
  • Blocks the merge with a PR comment · re-run to clear
Content Library · Read while it's still free

Learn the depth.
System design · Golang · Multi-cloud networking.

Tekton publishes four content streams: interactive System Design Labs on the canvas, a Go Data Structures Deep Dive series, AWS + multi-cloud networking case studies, and behind-the-scenes case studies from real production incidents.

· System Design Labs· Golang Deep Dive· AWS Networking· Multi-Cloud
  • SYSTEM DESIGN LAB

    How Kafka handles millions of messages a second

    A canvas-based lab: draw the Kafka architecture piece by piece with an agent walking beside you — brokers, partitions, ISR, exactly-once semantics.

    COMING SOON
  • GOLANG DEEP DIVE

    Goroutines & Channels — how Go's runtime scheduler actually works

    The runtime scheduler, GMP model, work-stealing, and why goroutines are cheap. From `go func()` to preemption. Part of the Go Data Structures Deep Dive series.

    COMING SOON
  • GOLANG DEEP DIVE

    Escape Analysis — when Go allocates on the heap vs the stack

    Every allocation is a story. Learn to read the compiler's mind with `go build -gcflags="-m"` and write code that stays on the stack.

    COMING SOON
  • AWS NETWORKING

    VPC deep dive — subnets, route tables, and the path of a packet

    Route table lookups, NACL vs SG evaluation order, ephemeral ports, and why your Lambda can't reach RDS. A packet-eye view of AWS networking.

    COMING SOON
  • MULTI-CLOUD

    Connecting AWS, GCP, and Tencent over Megaport — the real path

    Transit Gateway attachments, BGP peering, MTU pitfalls, and how a Lambda in AWS talks to an on-prem OMS in Bangkok. Case study from production.

    COMING SOON
  • SYSTEM DESIGN LAB

    How the Kubernetes scheduler decides where your pod lands

    Filter → score → bind. Draw the scheduler pipeline on the canvas and see why your pod is pending. Predicates, priorities, taints, tolerations.

    COMING SOON
Pricing · BDT · Cohort 1

One course.
Two ways in.

Cohort 1 starts 25 July 2026 · Tue / Thu / Sat · 9–11 PM Dhaka. 20 weeks. 60 live classes. You ship the whole Brain — correlation engine, skill harness, MCP layer, Sherlock in Slack.

Pay in full · fastest confirmation

Standard Enrollment

৳20,000
one-time · everything included
  • Full 20-week live cohort · 60 classes
  • All 8 phases · you ship the whole Brain
  • $1,000 Anthropic-grade AI tokens ($200/mo × 5)
  • Puku CLI bundled with the course
  • Tekton Labs Discord + private cohort channel
Enroll · ৳20,000
50% off · challenge only
Prove it · pay half

Challenge — 50% Off

৳20,000৳10,000
if you solve the challenge
  • Everything in Standard — same course, same tokens
  • Solve a small platform-engineering challenge
  • Question mailed to you within 24h of applying
  • Submit answer in Tekton Labs Discord
  • Pass → pay ৳10,000 · fail → still ৳20,000 offer
Apply for the challenge
THE CHALLENGE · HOW 50% OFF WORKS

Solve one real problem. Pay half.

We give you a platform-engineering problem — the kind you'll solve every week in the course. Answer it well, and your seat is ৳10,000 instead of ৳20,000. Simple.

  1. 01
    Apply
    Fill the challenge form — name, email, GitHub / LinkedIn. Takes 30 seconds.
  2. 02
    Question in your inbox
    Within 24h you get one platform-engineering problem — real scenario, not a leetcode puzzle. Datadog trace + K8s event + a broken deploy — write the RCA.
  3. 03
    Submit in Discord
    Post your answer in the #challenge channel of the Tekton Labs Discord. Instructor grades it live, gives feedback either way.
  4. 04
    Pass → 50% off
    Solve it well → payment link for ৳10,000 arrives. Miss it → the ৳20,000 seat is still yours if you want it.

Answers are submitted in #challenge on Discord. Even if you fail, you get feedback + can still take the ৳20,000 seat if you want it. No pressure — the challenge is opt-in.

Registration opens via Google Form. Payment: bKash / Nagad / bank transfer. Payment link + Discord invite arrive within 24h of your application.

Questions people actually ask

The honest ones.
Nothing hidden until the checkout page.

  • No. Half the course is Platform Engineering fundamentals — CLI design, Kubernetes internals, cloud networking, observability. The AI parts (MCP, agents, knowledge graphs) are taught from first principles, starting from `pip install`. If you can ship a REST service, you have enough.

Still unsure? Email me directly— I read every one.
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