The autonomous engineering harness

From ticket to production, handled.

A harness, not a copilot. Fourteen specialist agents take each change from ticket to production inside your cloud, with your people on every gate that matters.

Start a pilot See how it works
confixa: payments-api L3 · gated prod
$confixa run PAY-2841 --service payments-api
✓orchestratorPAY-2841 duplicate refunds · sev-20.4s
✓architectrefund path traced · 6 tasks planned3.1s
✓forgefix/refund-idempotency · +182 −3712.8s
✓prover14 tests added · mutation 91%9.6s
✓aegisCVE-2026-3311 reachable · patched6.2s
✓ledgerevidence signed · ISO · DPDP · CERT-In1.4s
✓curatorPR #1184 reviewed and merged4m 12s
✓conductorcanary 5% · p99 142ms · SLO held11m
?Promote payments-api to 100% of traffic? (y/N) y
✓youapproved in slack#payments-deploys
✓conductorpromoted to 100% · rollback armed2.0s
✔ run 4f21c complete in 20m 14s · trace saved
fix/refund-idempotency run 4f21c · 8 agents · 1 approval

Plugs into the stack you already run

GitHub GitLab Bitbucket Jira Linear ClickUp GitHub Actions Jenkins Argo CD Terraform Pulumi Kubernetes GKE EKS AKS OpenTelemetry Prometheus Grafana Datadog Semgrep Trivy Snyk StackHawk HashiCorp Vault Secret Manager PagerDuty Slack Microsoft Teams BigQuery Snowflake

The problem

Your lifecycle is disconnected.

Forty tools, twelve handoffs, and a context reset at every boundary. The work isn't hard; the seams are. Every seam is a queue, and every queue is where your quarter goes.

Delivery

12

Handoffs per feature

Development, QA, security, operations, compliance and finance each pick up the same change and rebuild its context from scratch.

Security

800→ 8

Findings, eight that matter

Scanners report everything they find. Establishing which findings are actually reachable in production is where security time goes.

Compliance

3wks

Of audit preparation

Evidence is rebuilt by hand from tickets, chat history and screenshots, typically twice a year and under deadline.

AI adoption

0

Visibility into your agents

AI is being adopted across the lifecycle with no trace, no spend cap and no replay. That gap is where the next incident starts.

What changes One governed loop carries each change across every boundary. Context, evidence and approvals travel with it, so nothing waits for a handoff. See it run →

What “handled” means

Every stage, owned end to end.

Each stage is run by the specialist that owns it, with context, evidence and approvals travelling with the change. One decision is left to you: promotion to production.

  1. 01

    Planned

    Spec, ADRs and an ordered task graph.

    Architect Done · 38s
  2. 02

    Built

    Test-first code in an isolated worktree.

    Forge Done · 2m 10s
  3. 03

    Tested

    Unit to end-to-end, proven by mutation testing.

    Prover Done · 1m 04s
  4. 04

    Secured

    Scanned, triaged for reachability, patched.

    Aegis Done · 52s
  5. 05

    Evidenced

    Signed evidence mapped to every framework.

    Ledger Done · 14s
  6. Your approval

    Shipped

    Canary release with auto-rollback on SLO breach.

    Conductor Done · 11m
  7. 07

    Observed

    SLOs, dashboards and a replayable trace.

    Lantern · Choir Done · live
  8. 08

    Optimised

    Cost and performance attributed per change.

    Abacus Done · 21s
Runs in your VPC Graph-engineered context Bring your own model Human gates on prod CERT-In · DPDP · ISO 27001

The harness

Six planes that make autonomy safe.

Confixa, from the Latin configere: to fasten together. The agents do the work; the harness decides what they may touch, records what they did, and stops where a person must decide.

01 · Context

Context plane

A persistent, versioned estate graph of your services, dependencies, controls, incidents, owners and cost. Every agent queries it before acting, so no task starts from zero. Context becomes an asset, not a prompt.

02 · Tools

Tool plane

Every capability is a typed tool with least-privilege credentials scoped to a single agent. An agent can do only what it was explicitly handed, and every call is recorded.

03 · Policy

Policy plane

Policy-as-code defines what each agent may touch, in which environment, at what spend, and what needs a human. Autonomy becomes a configuration you review in a pull request.

04 · Trace

Trace plane

Every decision, tool call and diff is recorded as a span. Any run replays end to end, so “why did it do that?” takes thirty seconds to answer, not a forensic investigation.

05 · Approval

Approval plane

Production promotion, schema migrations, IAM and KMS changes, data deletion and spend over threshold all stop at a human gate. Approve in Slack or Teams, with the diff and simulated blast radius attached.

06 · Sandbox

Sandbox plane

Work runs in isolated git worktrees, ephemeral preview environments and sealed networks. Blast radius is bounded by design, not by good intentions.

The agents

One orchestrator. Fourteen specialists.

Fourteen focused agents, each accountable for one discipline end to end, coordinated by an orchestrator that holds the plan, the budget and the gates.

Master orchestrator

The one that holds the thread

Turns intent into a dependency graph, routes each task to the specialist that owns it, holds the spend envelope and stops at every human gate. When it is unsure, it escalates rather than guesses.

  • Plans
  • Routes
  • Budgets
  • Gates
I

Build

Turns intent into tested, reviewed code.

Architect
Design & specs

Reads the codebase, writes the specification and ADRs, designs schemas and API contracts, and breaks each epic into an ordered task graph.

Forge
Implementation

Implements test-first, refactors, and runs data migrations and dependency upgrades in isolated worktrees, so parallel tasks never collide.

Prover
Testing & QA

Builds unit, integration, contract, end-to-end and visual tests, then uses mutation testing to prove they catch real defects.

Curator
Git & review

Keeps branches clean, writes and reviews pull requests, runs the merge queue, resolves conflicts and maintains versions and changelogs.

II

Ship

Moves a merged change safely into production.

Conductor
CI / CD

Authors and repairs pipelines, signs every artifact with an SBOM, and runs canary or blue/green releases with automatic rollback on SLO breach.

Atlas
Infrastructure

Writes Terraform and Pulumi, detects and corrects drift, provisions environments and creates a preview environment for every pull request.

III

Secure

Proves every change is safe and compliant.

Aegis
Security / SecOps

Runs SAST, DAST, SCA, secrets and container scans, then applies reachability triage so teams fix the findings an attacker could actually use.

Ledger
Compliance

Maintains one controls library, collects signed evidence continuously, gates pipelines on policy and flags control drift the day it happens.

IV

Observe

Watches the service, the model and the agents.

Lantern
App & infra telemetry

Adds OpenTelemetry instrumentation, defines SLOs, builds dashboards and tunes noisy alerts down until every page is worth answering.

Prism
LLM observability

Tracks token cost and latency per route, gates prompt changes with evals, and detects drift, hallucination, PII leakage and prompt injection.

Choir
Agentic observability

Watches the agents themselves: trajectory traces, loop detection, budget caps and replay of any run. The basis for trusting autonomy in production.

Medic
Incidents & SRE

Triages incidents, finds root cause, runs remediation runbooks, writes the postmortem while context is fresh and enforces error budgets.

V

Sustain

Keeps cost, performance and upkeep in check.

Abacus
FinOps & performance

Attributes cost to each service and pull request, right-sizes infrastructure, profiles slow queries and opens the change that removes the waste.

Custodian
Maintenance

Keeps dependencies and CVEs current, plans end-of-life upgrades, removes dead code and keeps documentation in step with the code.

+

Yours

Your own agents, under the same harness.

Your agent here
Bring your own

Register an agent your team already built. It inherits the context, policy, trace and approval planes, and appears in Choir alongside the mesh.

How the custom harness works

A day in the loop

One ticket, one human decision.

A real trace: a refund idempotency bug in a payments service. Scroll to follow it from Jira to a hundred percent of production traffic.

Step 01 of 09

Prod traffic 0%

Sense read the world
Plan decompose the work
Simulate dry-run and price it
Act change something
Verify prove it held
Learn write it back
01

The ticket lands

Orchestrator

PAY-2841 · duplicate refunds on retry. Confixa already holds the context: this service, its 412 files, its owners, its SLOs, the two incidents it caused last quarter. Nothing needs re-explaining.

02

A plan, not a guess

Architect

Traces the refund path across three services, finds the missing idempotency key at the boundary, writes the spec and an ADR, and decomposes it into six tasks with an explicit dependency order.

03

Dry run before anything moves

Orchestrator

Prices the run, checks it against policy, and estimates blast radius. A migration is involved, so the gate for step 08 is registered up front instead of being discovered at deploy time.

04

Code, test-first

Forge · Prover

A failing test that reproduces the double refund, then the fix: +182 −37 across four files. Fourteen new tests. Mutation score 91%, so the tests are load-bearing rather than decorative.

05

Scanned where it counts

Aegis

SAST and DAST clean. One transitive dependency carries CVE-2026-3311. Reachability analysis confirms it is live on this code path, so it is patched in the same branch rather than filed as ticket 801.

06

Evidence, automatically

Ledger

Signed evidence lands against ISO 27001 A.8.28, DPDP §8(5) and the CERT-In log-retention control. Same artifact, three frameworks. No screenshots, no scrollback archaeology.

07

Reviewed and merged

Curator · Conductor

A pull request a human can actually read: the reasoning, the diff, the risk note, the trace link. Through the merge queue, pipeline green in 4m 12s, artifact signed, SBOM attached.

08

The one human decision

You

Canary at 5%: p99 142ms, error rate 0.01%, SLO held for eleven minutes. Confixa asks in Slack for the one decision it is not allowed to make, promote to 100%?, with the diff, the metrics and the rollback plan attached.

09

Then it gets smarter

Lantern · Abacus · Choir

Fresh dashboards and an SLO for the refund path. Abacus notices the retry storm was costing ₹18k a month and opens a follow-up. Choir files the full trajectory into the context plane, replayable, costed and attributable, so the next fix starts from here.

Knowledge graph

A graph that gets denser every week.

Services, dependencies, controls, incidents and owners, indexed as one living graph that never stops growing. Every run, review, deploy and scan writes back new nodes and edges, so Confixa reasons about your estate a little better every week it runs.

Sample estate: payments platform Hover to inspect · click to trace blast radius · drag to rearrange

Autonomy follows the graph

The ladder is not a calendar; it is a consequence. A service earns its next rung when the graph knows enough about it to make the risk legible. Trust accrues; it is never simply asked for.

  1. Day 1

    Index

    Repos, cloud, pipelines and telemetry become nodes and edges. An accurate map, still shallow.

    L0 Observe
  2. Weeks 2-6

    Annotate

    Reviews, test results, scan verdicts and deploy outcomes attach to the things they judged.

    L1 → L2 Suggest, act in non-prod
  3. Months 2-6

    Anticipate

    Enough history to see patterns: which changes break, which alerts never matter, what a fix will cost.

    L3 Act in prod, gated
  4. Compounding

    Inherit

    The graph now encodes your organisation’s judgement. A new service starts with your conventions, controls and rotas.

    L4 Autonomous in envelope

The same reason it can't be bought as a finished product, and the same reason switching costs are real: this graph is yours, and it took your traffic to build.

the question every agent asks before it acts
-- "what breaks if I patch this, and who has to know?"
MATCH (d:Dependency { cve: 'CVE-2026-3311' })
      <-[:DEPENDS_ON*1..3]- (s:Service)
MATCH (s)-[:GOVERNED_BY]->(c:Control)
MATCH (s)-[:OWNED_BY]->(o:Owner)
WHERE  reachable(d, s)                -- not merely present: actually callable
RETURN s.name, s.autonomy, blast_radius(s), collect(c), o.rota
ORDER BY blast_radius(s) DESC

before acting

Blast radius, not guesswork

Every plan is costed against the graph first. “This touches two services, one control and the on-call rota” is a sentence a person can approve in seconds.

security

Reachability, not inventory

A CVE in a lockfile matters only if a live path reaches it. Graph traversal is how Aegis separates the eight findings that matter from the eight hundred that don't.

compliance

One control, many systems

Controls are edges, not spreadsheet rows. Which systems a control covers, and which controls a system lacks, become one query answered against today's estate.

over time

It compounds

Every run leaves the graph richer than it found it. The hundredth deploy of a service is planned with far more context than the first, and that gain never resets.

The autonomy ladder

Autonomy is a dial, not a leap of faith.

Every service starts at L0 and is promoted on evidence, one rung at a time. Set it per service: your billing core can sit at L1 while your internal tooling runs at L4. Breach the envelope and the service drops a rung automatically and pages a human.

Level 0 · week one on any estate

Observe

Confixa reads your repos, cloud, pipelines and telemetry and writes nothing at all. In return you get a service catalogue, a tech-debt ledger, a security baseline, a control-gap report and a cost-waste report. It is useful on its own, which is exactly how trust is earned.

At this level

Observability

Watch the app, the model and the agents.

Classic monitoring tells you the service got slow. LLM observability tells you the model got worse. Agentic observability tells you your automation is misbehaving. In an autonomous organisation, that third layer is the one that protects you.

Application

lantern

The service layer, instrumented for you rather than by you.

  • OpenTelemetry instrumentation written into the code
  • SLOs and error budgets per service
  • Alerts tuned down until every page means something
  • Anomaly detection tied to the deploy that caused it

LLM

prism

Turns “the AI feature got worse” into a measurable regression.

  • Token cost and latency per route, per prompt version
  • Eval harness that gates prompt changes like code
  • Groundedness, hallucination and PII-leakage scoring
  • Prompt-injection and jailbreak detection at the edge

Agentic

choir

The layer that makes autonomy approvable by your security team.

run 4f21c · 6m 04s · ₹41
├─ architect · plan ✓
│  └─ 6 tasks · 3 parallel
├─ forge · 4 files ✓
├─ aegis · 1 patched ✓
└─ conductor · gate → human
  • Every decision, tool call and diff as a replayable span
  • Loop, thrash and runaway detection with hard stops
  • Blast-radius accounting and per-agent budget caps
  • Time-travel replay: rerun any decision, see why

Compliance

One control. Four frameworks.

A unified controls library where a single piece of signed evidence satisfies CERT-In, the DPDP Act, ISO 27001 and SOC 2 at once. Built India-first, because that's the ground we know best.

The six-hour clock, handled

CERT-In requires incident reporting within six hours. Medic detects, Ledger drafts the filing with the evidence already attached, a human signs it.

Evidence with a trace link

Every check emits signed, timestamped evidence pointing at the exact run that produced it. The audit pack assembles itself.

Policy-as-code in the pipeline

Controls aren't a document. They're gates: fail the control, fail the build. Drift raises a ticket the day it appears.

A posture board you can show a client

Live control coverage, per environment, per framework. The best slide in your security questionnaire response.

Controls · payments-api · prod 42 / 44 green
Encryption at rest, all data stores
ISO A.8.24DPDP §8(5)SOC2 CC6.1
PASS
Immutable audit logging, 180 days
CERT-In 5(iv)ISO A.8.15
PASS
Vulnerability remediation SLA
ISO A.8.8SOC2 CC7.1PCI 6.3
PASS
Least-privilege review, service accounts
ISO A.5.18SOC2 CC6.3
DRIFT
Incident reporting, 6-hour window
CERT-In 5(i)DPDP §8(6)
PASS
Consent & purpose limitation on PII
DPDP §6GDPR Art.5
PASS

Custom harness

Opinionated, then yours by week two.

Confixa ships with a roster and a point of view. It is still a harness, not a cage: every plane is extensible, and the whole contract for a service is one reviewable file.

  • Your model

    Frontier, open-weight or self-hosted, routed per task class: efficient models for mechanical work, frontier models for architecture and security. Your keys, your quota.

  • Your agents

    Register an agent your team already built. It inherits the context, policy, trace and approval planes unchanged, and appears in Choir with the rest of the mesh.

  • Your tools

    Expose any internal API as a typed tool with scoped credentials and an audit trail. Existing deploy scripts become governed, first-class capabilities.

  • Your skills

    Encode your deploy process, review checklist and naming conventions as versioned skills the mesh is required to follow. Institutional knowledge that executes.

confixa.yaml: the whole contract for one service
# One file per service. Reviewed in a PR like anything else.
service: payments-api
autonomy: L3            # observe · suggest · non-prod · gated-prod · envelope

model:
  reasoning: byo/frontier        # architecture, security, RCA
  mechanical: byo/efficient      # lint, docs, dep bumps

agents: [ architect, forge, prover, curator, conductor,
          aegis, ledger, lantern, medic, abacus ]

envelope:
  budget: { usd_per_day: 40, tokens_per_run: 400_000 }
  may_touch: [ staging/*, prod/payments-api ]
  never: [ iam:*, kms:*, db:drop*, billing:* ]

gates:
  human_approval: [ prod_promote, schema_migration, spend > 250 ]
  approvers: [ "@platform-oncall" ]
  respond_in: slack#payments-deploys

rollback:
  on: [ slo_breach, error_rate > 0.5%, cost_spike > 30% ]
  within: 90s

compliance:
  packs: [ cert-in, dpdp-2023, iso-27001 ]
  evidence: signed

Where Confixa sits

Best-in-class tools. We work the span.

Most of these are best-in-class at what they do, and Confixa is designed to sit alongside them: we read their findings and act on them. What we add is the connective tissue: one governed loop so a change doesn't stop and wait at every boundary.

  • Cloud security

    Wiz, Orca
    Deep atRuntime posture and misconfiguration, at real depth
    How Confixa fitsWe consume their findings and carry them back to a merged fix.

  • Compliance automation

    Drata, Vanta
    Deep atEvidence collection and questionnaire workflow
    How Confixa fitsWe generate the evidence upstream and remediate the failing control.

  • AI coding

    Copilot, Cursor
    Deep atWriting code with you, in the editor
    How Confixa fitsWe take the same work onward from the pull request to production.

  • CI/CD platforms

    GitHub, GitLab, Harness
    Deep atRunning pipelines reliably at scale
    How Confixa fitsWe author, repair and reason about the pipelines that run on them.

  • IT services partners

    The large SIs
    Deep atBreadth, domain depth and accountability
    How Confixa fitsWe handle the repeatable loop so their people work higher up.

  • Your platform team

    In-house
    Deep atKnowing your estate better than anyone
    How Confixa fitsWe give them the harness so they don't have to build one first.

Confixa

One governed loop, with the gates you deliberately keep.

  1. Ticket
  2. Code
  3. Tests
  4. Security
  5. Compliance
  6. Ship
  7. Observe
  8. Optimise

Design partner program

Three phases, useful from week one.

Month one delivers value whether or not you adopt autonomy: observe-only produces a real audit of your estate. Every step after that is your decision, one service and one rung at a time.

Days 1-30

Observe

Connect repos, cloud, pipelines and telemetry. Confixa runs at L0 and writes nothing.

  • Service catalogue and dependency map
  • Tech-debt ledger, ranked by blast radius
  • Security baseline with reachability triage
  • Control-gap and cloud-waste reports

Days 31-60

Assist

Promote two chosen services to L1 and L2. The agents start doing real work you review.

  • Real pull requests, reviewed by your team
  • Pipeline repaired, missing tests written
  • Instrumentation and SLOs where there were none
  • Staging owned end to end by the mesh

Days 61-90

Operate

One service to L3: production canaries with your human promotion gate.

  • Gated production deploys with auto-rollback
  • Compliance evidence flowing continuously
  • Full agentic trace of everything the mesh did
  • A promotion decision backed by evidence

14

Specialist agents across five planes, one orchestrator holding the thread.

5

Autonomy rungs, set per service and promoted only on evidence.

6

Governance planes: context, tool, policy, trace, approval and sandbox.

90

Days from first connection to a gated production deploy.

Structural facts about the product, not customer outcomes. Confixa is in its design-partner phase, so we would rather show you a live trace than quote a number we cannot source.

Straight answers

The questions your CISO will ask.

Access, safety, where it runs and what it proves, answered plainly. Hover a question to read the answer.

How is this different from an AI coding assistant?

Assistants are genuinely excellent at what they do: writing code with you, in the editor. Confixa is aimed at a different part of the problem: it picks the same unit of work up at the pull request and carries it onward through testing, security scanning with reachability triage, compliance evidence, pipeline changes, a canary deploy, telemetry and cost review, as one governed loop with a single audit trail. Most teams use both, and we'd expect you to. The difference isn't intelligence, it's span and governance.

Does Confixa need production access?

No, neither to start nor to be useful. Every service begins at L0 with read-only access. Production write access only exists from L3 upward, is scoped by policy-as-code you review in a pull request, and always passes through a human gate for promotion. Many teams keep most services at L2 indefinitely, because that is where most of the toil lives.

Where does it run, and who holds the keys?

Your cloud, your credentials. SaaS, a private install in your own GKE project, or fully air-gapped. Bring your own model and your own secret manager. Confixa never needs to hold a long-lived production credential. It requests scoped, short-lived tokens per task, and every issuance is logged.

What stops an agent from doing something catastrophic?

Five things, in order: the sandbox plane means it's working on a copy; the policy plane means destructive verbs are simply absent from its toolset; the simulate beat prices and bounds the change before it happens; the approval plane blocks the irreversible operations outright; and Choir enforces hard stops on loops, thrash and budget overrun. Then rollback is automatic on SLO breach. We hold ourselves to all six; that is the bar we believe production autonomy deserves.

What exactly is agentic observability?

Observability pointed at the agents rather than the application. Every decision, tool call and diff becomes a replayable span, with loop detection, blast-radius accounting, per-agent budget caps and time-travel replay. It answers "what did your AI do in my estate last quarter, and what did it cost?" Without an answer to that, no security team will approve autonomy in production. It's the permission slip, not a feature.

Which compliance frameworks are covered?

The India pack covers CERT-In directions including the six-hour incident reporting clock, the DPDP Act 2023, and ISO 27001:2022. The global pack adds SOC 2 Type II, PCI-DSS, HIPAA and GDPR. Controls are unified rather than duplicated, so one signed piece of evidence can satisfy several frameworks at once. That is what turns audit preparation from weeks of work into a download.

Can we bring our own agents and tools?

Yes, and we'd encourage it. Register an existing agent and it inherits the context, policy, trace and approval planes without changes. Register any internal API as a typed tool with scoped credentials. Encode your own deploy rituals and review checklists as versioned skills the mesh must follow. The harness is the product; the fourteen agents are a starting roster.

What happens to my engineers?

They stop being a queue and start being reviewers, architects and owners of the envelope. In practice the job shifts from writing the boilerplate and chasing the pipeline to deciding what should be true, then approving the changes that make it true. Teams that adopt this well do not shrink; they stop carrying a twelve-month backlog.

Still have a question? A walkthrough with the team that builds Confixa takes thirty minutes.

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Start read-only. Decide on evidence.

Thirty days read-only, and you keep a full audit of your estate whatever you decide. Our team reviews every application within two working days.

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