Build your agent workforce with a founder-led team that builds on one
AgentTask Consultancy is the services arm of AgentTask: we design, build, and operate AI-agent workflows — on our platform and in your own stack. Every engagement ships working software plus the playbooks to keep running it without us.
Consulting engagements
Agent discovery workshop
Map your real workflows to what agents can own today: we inventory the busywork, rank automation candidates by value and risk, and prototype the first agent live against your backlog.
You get: A prioritized use-case roadmap with success metrics, plus a working first prototype.
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What's included
- A facilitated session (half-day to two days) with the people who own the workflows
- Inventory of recurring work: what agents can own today, assist with, or should not touch
- Value-and-risk ranking of every automation candidate, with success metrics per candidate
- A live prototype: we stand up a first agent against your actual backlog before we leave
How it works
We prepare with a short questionnaire, run the session on your calendar (remote or on-site), and close with a working prototype and a written roadmap — not a slide deck of maybes.
A good fit when
- You know agents should be doing some of your work but nobody agrees on where to start
- You want evidence before budget: the prototype is the argument
- Leadership needs a shared, prioritized picture of what agentic work means for your team
You walk away with
A prioritized use-case roadmap with success metrics, and a first agent your team watched get built.
Agent readiness audit
A structured assessment of your workflows, data quality, integration points, and governance appetite before you commit to building — where agents will pay off first, and where they will not.
You get: A readiness report with a ranked pilot shortlist and the guardrails each one needs.
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What's included
- Workflow mapping: where work enters, who routes it, what gets dropped
- Data and integration review: what your systems can actually expose to an agent, safely
- Governance appetite: approval flows, audit requirements, and human-in-the-loop boundaries
- A ranked pilot shortlist — and an explicit list of places agents will not pay off yet
How it works
Two to four weeks, fixed scope. We interview the owners, read the systems, and pressure-test each candidate against data quality, integration effort, and risk before anything gets built.
A good fit when
- You are choosing between several possible agent projects and can only fund one first
- A previous automation effort stalled and nobody wrote down why
- Compliance or audit requirements mean guardrails must be designed before the build
You walk away with
A readiness report with a ranked shortlist, the guardrails each candidate needs, and a build/wait/skip call on every workflow we assessed.
Agent pilot build
One scoped workflow taken to production quality against your real systems: human-in-the-loop checkpoints, baseline metrics agreed up front, and an honest go/no-go at the end.
You get: A working pilot, its evaluation framework, and a production recommendation.
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What's included
- One scoped workflow taken to production quality against your real systems
- Human-in-the-loop checkpoints wherever the workflow touches something irreversible
- Baseline metrics agreed before we write a line — so "did it work" has a number
- An evaluation framework your team keeps running after the pilot ends
How it works
Roughly eight to twelve weeks, fixed scope. We build in your environment, demo weekly against the agreed metrics, and finish with an honest go/no-go recommendation — including "no."
A good fit when
- The audit (ours or yours) already identified the workflow worth proving
- You need a production-grade answer, not a demo that dies in staging
- Stakeholders want evidence with numbers before a wider rollout
You walk away with
A working pilot, its evaluation framework, and a written production recommendation you can take to leadership.
Codebase migration by agent fleet
Legacy modernization run as governed agent work: agents analyze the old codebase, document undocumented systems, and convert it incrementally — every run claimed, reviewed, and on the audit trail. Never big-bang.
You get: Migrated code landing through your normal review, with generated documentation and dependency maps.
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What's included
- Agent-driven analysis of the legacy codebase: structure, dependencies, dead ends
- Generated documentation for the systems nobody documented
- Incremental conversion in fixed-scope waves — each wave lands through your normal code review
- Every agent run claimed, attributed, and on the audit trail in AgentTask
How it works
A paid assessment sizes the codebase and defines wave one. Then agent fleets work the migration as governed backlog items: humans review every change, nothing lands big-bang, and progress is visible on the board — not in a status meeting.
A good fit when
- A legacy system everyone fears touching is blocking the roadmap
- The people who understood the old code are gone, and the documentation never existed
- A previous big-bang rewrite attempt failed or was cancelled
You walk away with
Migrated code that arrived through your own review process, generated documentation, and dependency maps for whatever remains.
MCP server development & integration
Model Context Protocol servers built as secure gateways to your internal systems — ERP, CRM, databases — with the auth, logging, and access control to let AI tools in safely. MCP is the protocol AgentTask itself speaks in both directions.
You get: Deployed MCP servers with an access-control layer and integration documentation.
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What's included
- Model Context Protocol servers built as secure gateways to your internal systems — ERP, CRM, databases, internal APIs
- The auth, logging, and access-control layer that makes exposing those systems to AI tools defensible
- Integration documentation your own engineers can extend
How it works
Fixed-scope build per system, with an optional support retainer after. MCP is the open protocol the AI ecosystem is converging on — and the one AgentTask itself speaks in both directions, so this is home turf.
A good fit when
- Your team's AI tools need internal data but security keeps (rightly) saying no
- You are standardizing on MCP and want the first servers built by people who run one in production
- Point-to-point integrations are multiplying and you want one governed gateway pattern instead
You walk away with
Deployed MCP servers with a real access-control story, and the documentation to build the next one yourselves.
AI coding-tool rollout & enablement
Claude Code and Cursor across a real team — rules files, slash commands, agent definitions, MCP integrations into your internal systems, review hooks, and adoption you can measure. Rollout, not just licenses.
You get: A configured tooling baseline, a trained team, and adoption telemetry.
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What's included
- Claude Code / Cursor rollout across a real team — not just license distribution
- Rules files, slash commands, and agent definitions tuned to your codebase and conventions
- MCP integrations connecting the tools to your internal systems
- Review hooks and adoption telemetry, so you can measure usage instead of guessing
How it works
Either a hands-on workshop or a six-to-ten-week engagement, depending on team size. We pair with your engineers on their actual tickets — the configuration that survives is the one built on real work.
A good fit when
- You bought the licenses and adoption flatlined after week two
- Every engineer has invented a private workflow and none of it is shared
- You want the tooling wired to your systems, standards, and review culture
You walk away with
A configured tooling baseline the team actually uses, and telemetry showing whether that stays true.
Custom agents & automations
Purpose-built agent workflows integrated with the platforms you already run — inside AgentTask (crews, triggers, board automations) or entirely outside it. You own the code and the IP.
You get: A production agent system with monitoring, handed over with its playbook.
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What's included
- Purpose-built agent workflows integrated with the platforms you already run
- Inside AgentTask: crews, triggers, and board automations on your governed backlog
- Outside it: standalone agent systems in your own stack, on your own infrastructure
- Orchestration, monitoring, and an evaluation harness — not just a prompt in a loop
How it works
Fixed-scope build with weekly demos, then an optional retainer for tuning. Everything is handed over with its playbook; you own the code and the IP outright.
A good fit when
- The workflow you need does not exist off the shelf in any tool
- An existing manual process is well-understood and ready to be delegated
- You want the build done by a team that operates agents daily, then handed over cleanly
You walk away with
A production agent system with monitoring, its playbook, and full ownership of everything we built.
Embedded AI engineering
A senior engineer inside your sprints, your codebase, and your cloud, directed by your roadmap. The forward-deployed model: we make it work in your environment, you keep the architecture.
You get: Shipped production systems in your infrastructure, on your priorities.
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What's included
- A senior engineer embedded in your sprints, codebase, and cloud
- Direction comes from your roadmap; we bring the agentic-engineering experience
- Production delivery inside your infrastructure, at your standards
- Knowledge transfer as a first-class deliverable, not an afterthought
How it works
A flat monthly arrangement — the forward-deployed model, no hourly meters. Start with one engineer, scale to a pod if the roadmap demands it, stop when your team can run it alone. You keep architectural ownership throughout.
A good fit when
- The roadmap is clear but the agentic-engineering experience is not in-house yet
- Hiring for this profile is slow and the work cannot wait
- You want capability transferred into your team, not a permanent dependency
You walk away with
Shipped production systems in your own infrastructure — and a team that learned how by working alongside us.
Types of agents we build
The agent landscape in the language buyers actually use — what each type owns, where it stands in production, and how we build it responsibly. Every build ships with tiered autonomy, human-in-the-loop gates, an audit trail, and an evaluation harness as standard.
Coding & software agents
Agents that write, review, refactor, and land code from a ticket — the most-used agent category in day-to-day work.
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What it owns
- Implementing features from a ticket and opening the PR
- Automated code review and bug triage
- Refactors and dependency upgrades run as reviewable changes
Where it stands
The most heavily used agent category in practice: industry surveys report large shares of committed code are now agent-assisted, and coding agents top the tools engineers use daily. This is also home turf — AgentTask exists so coding agents and humans share one backlog.
How we build it
- Agents work claimed backlog items; every run links to its branch and PR
- Human review stays the merge gate — the agent earns trust, it is not granted it
- Adoption telemetry so you know what the agents actually shipped
Customer support agents
Conversational agents resolving tickets end to end across chat and email — a common first production use case for agents.
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What it owns
- Tier-1 resolution: order status, refunds, account questions
- Case triage with full context, escalating what it should not touch
- Drafting responses for human review in higher-risk queues
Where it stands
One of the most common production agent patterns in industry surveys; resolution rates vary widely by queue, so we agree a measurable target with you before building.
How we build it
- Tiered autonomy: read-only, then reversible actions, then human approval for anything external or irreversible
- Every resolution on an audit trail, with escalation rules designed up front
- Evaluation harness against your real historical tickets before it meets a customer
Research & knowledge agents
Agents that search, read, and synthesize across the web and your internal corpus into grounded, cited briefs.
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What it owns
- Market and competitor briefs with sources attached
- Due-diligence packs assembled from many documents
- Internal knowledge Q&A over wikis, drives, and past decisions
Where it stands
The #2 production category in the 2025 surveys (combined with data analysis). Retrieval quality and grounding discipline separate the useful ones from the confident-and-wrong ones.
How we build it
- Grounding first: answers cite their sources or say they cannot
- Connected to your systems through governed MCP gateways, not ad-hoc scraping
- Output lands as reviewable artifacts on the backlog — not ephemeral chat
Data & analytics agents
Text-to-SQL and BI agents that query your warehouse, build analyses, and explain what changed.
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What it owns
- Self-serve questions answered directly from the warehouse
- Recurring reporting and dashboard upkeep
- Anomaly explanation: not just that a metric moved, but why
Where it stands
A fast-growing category with heavy vendor investment across the warehouse ecosystem. The hard part is semantic correctness — an agent that writes plausible-but-wrong SQL is worse than none.
How we build it
- A semantic layer and query review before autonomy widens
- Evals on known-answer questions from your own data
- Read-only warehouse credentials by default; writes are a separate, gated decision
Workflow & orchestration agents
Digital coworkers on a queue: agents coordinating multi-step processes across your systems — the fastest-growing enterprise pattern.
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What it owns
- Intake, verify, route, report chains across SaaS tools
- Employee onboarding/offboarding sequences
- Procurement, approvals, and other cross-system busywork
Where it stands
A common agent pattern inside large enterprises. Usually multi-agent, always integration-heavy.
How we build it
- The queue is a governed backlog: every step claimed, attributed, and auditable
- Systems connected through MCP gateways with real access control
- Human checkpoints exactly where the process is irreversible — and nowhere else
Document-processing agents
Agentic document intake: read mixed-format documents, extract, validate, and act — the successor to OCR pipelines.
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What it owns
- Claims intake from full submission packets — forms, photos, reports
- Invoice and AP processing across thousands of supplier formats
- Contract data extraction with validation against source systems
Where it stands
Among the earliest enterprise agent wins (AP and records management), now running in production back offices broadly. Boring, measurable, and high-ROI.
How we build it
- Validation rules and confidence thresholds decide what needs human eyes
- Every extraction traceable back to the exact source passage
- Exception queues on the backlog, so nothing silently drops
Sales & CRM agents
Outbound research, lead qualification, and CRM hygiene — with an honest caveat about full autonomy.
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What it owns
- Researching prospects and drafting personalized outreach
- Inbound lead qualification and meeting booking
- CRM enrichment and post-call hygiene nobody does by hand
Where it stands
A large, fast-moving market — and the category with the biggest gap between pitch and practice: industry data shows fully-autonomous outbound converts far below AI-plus-human hybrid. We build the hybrid.
How we build it
- The agent drafts and researches; a human sends — until the numbers argue otherwise
- CRM writes are validated and reversible
- Measured against your baseline conversion, not vendor benchmarks
QA & testing agents
Agents that generate, run, and self-heal end-to-end test suites — a category analysts now track on its own.
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What it owns
- Generating E2E tests from real user flows
- Self-healing selectors when the UI shifts
- Regression triage in CI: what broke, where, and since when
Where it stands
A fast-growing category. Vendors claim large gains in test-creation speed and maintenance effort; we treat those as claims and agree a measurable target with you before building.
How we build it
- Tests live in your repo and run in your CI — no external black box
- Flake triage as agent work on the backlog, with human sign-off on quarantines
- Coverage reporting that distinguishes generated from hand-written intent
Ops & incident agents
SRE agents that investigate production incidents — logs, metrics, traces — and propose or execute remediation.
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What it owns
- Root-cause analysis when the page fires: correlating logs, metrics, and traces
- Runbook execution with approval gates
- Post-incident writeups from what actually happened
Where it stands
A fast-growing category where vendors publish MTTR improvements; we treat those as claims and agree a measurable target with you before building. Trust is earned tier by tier.
How we build it
- Read-only investigation first; remediation actions gated behind human approval
- The agent's API and MCP requests are on the incident's audit trail
- Escalation on confidence drop — the agent knows what it does not know
Security & SOC agents
Autonomous alert triage and investigation for security operations — moving from concept to practical adoption.
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What it owns
- Phishing and alert triage at machine speed
- End-to-end investigation with synthesized evidence
- Threat-hunting sweeps humans never have time for
Where it stands
Analyst houses now describe AI SOC agents as practically adopted, with published results showing multiples of analyst throughput on triage. High stakes, so governance is the product.
How we build it
- The agent recommends; containment actions require human approval
- Full evidence chains attached to every verdict
- Measured on true-positive throughput and verdict accuracy, not alert volume
Browser & computer-use agents
Agents that drive real user interfaces where no API exists — the RPA replacement for portals and legacy systems.
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What it owns
- Filling government, supplier, and partner portals
- Legacy ERP data entry no one will ever build an API for
- Multi-site data collection on a schedule
Where it stands
Production-ready for narrow, well-defined flows; reliability is still the constraint for broad autonomy. The honest framing: high-value replacement for specific RPA scripts, not a general employee.
How we build it
- One flow at a time, with deterministic checkpoints and screenshots on the audit trail
- Fallback to human on any unexpected screen
- Where an API or MCP server is buildable, we build that instead — UI driving is the last resort
Codebase-migration agent fleets
Multi-agent systems that analyze, document, and translate legacy code at scale — a pattern well suited to agent fleets.
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What it owns
- Analyzer, converter, and dependency-mapper fleets over legacy codebases
- Language and framework migrations in reviewable increments
- Generated documentation for systems nobody documented
Where it stands
Early but proven at serious scale: published enterprise programs report millions of legacy lines processed and six-figure developer-hour savings. This is the pattern our migration engagement runs.
How we build it
- The fleet works a governed backlog: every run claimed, reviewed, audited
- Waves sized to your codebase, landing through your normal review
- See the codebase-migration engagement above — this is its engine
How engagements run
The ladder de-risks itself: each step is small, fixed-scope, and earns the next. Start wherever your situation actually is.
Workshop
Find the highest-value agent work.
Audit
Confirm readiness and the guardrails.
Pilot
Prove one workflow end to end.
Rollout
Scale the pilot: migrations, tool rollouts, custom agents.
Retainer
Keep it measured and improving.
How we work
- You own everything we build — code, integrations, IP, and the playbooks to run them.
- Deliverables persist: rules files, agent definitions, and MCP integrations keep working after we leave.
- Every engagement runs on a governed backlog with a live audit trail — the same discipline we sell.
- No big-bang rewrites: migrations land incrementally through your normal review.
Frequently asked questions
No. MCP servers, coding-tool rollouts, and custom agents can be built entirely in your own stack. Engagements that run on AgentTask get its governed backlog and audit trail as part of the deal, but the product is never a prerequisite.
We recommend starting with the discovery workshop or the readiness audit — a small, fixed-scope step that de-risks everything after it. Email us and we will suggest the right entry point for your situation.
You do. Code, integrations, documentation, and playbooks are yours. We are accountable for the outcome — not a dependency you rent forever.
Workshops are days; audits two to four weeks; pilots typically eight to twelve weeks; migrations proceed in fixed-scope waves sized to your codebase. Embedded engineering is a monthly arrangement you can stop at any time.
Scoped and quoted up front: workshops and audits are fixed-price, builds are scoped per statement of work, and embedded engineering is a flat monthly rate. No hourly meters.
Or start from the contact page — we reply to every inquiry.